Buyer profile management

ABSTRACT

Buyer profile management is described herein. In an example, a payment processing service can store, in a database, buyer profiles associated with buyers involved in transactions with one or more merchants of the payment processing service. The payment processing service can receive transaction data associated with a new transaction between a buyer and a merchant and can determine whether the transaction data corresponds to any of the buyer profiles. Based at least in part on determining that the transaction data corresponds to one of the buyer profiles, the payment processing service can update the database storing the buyer profiles, wherein updating the database comprises merging two or more buyer profiles. A recommendation can be made based on the merged buyer profile prior to completion of the new transaction.

RELATED APPLICATIONS

This Application claims priority to U.S. patent application Ser. No.15/445,619, filed on Feb. 28, 2017, now known as U.S. Pat. No.10,304,117, issued on May 28, 2019, which is a continuation of andclaims priority to U.S. patent application Ser. No. 14/289,469, filedMay 28, 2014, now known as U.S. Pat. No. 9,619,831, issued on Apr. 11,2017, which claims the benefit of U.S. Provisional Patent ApplicationNo. 61/969,720, filed Mar. 24, 2014, all of which are incorporatedherein by reference in their entireties.

BACKGROUND

People conduct transactions with many different merchants for acquiringmany different types of goods and services. Merchants, who are purveyorsof these goods and services, often perform transactions in person withtheir customers at a point of sale location. However, such merchants mayhave very little access to information about the overall shopping habitsof their customers, and even less access to information about theshopping habits of potential customers. Accordingly, it can be difficultfor merchants to obtain information that could assist the merchants ingrowing and improving their businesses, such as information related togoods and services that their customers are likely to purchase together.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items or features.

FIG. 1 illustrates an example environment for a payment and itemrecommendation service according to some implementations.

FIG. 2 illustrates an example environment for enabling point of saletransactions between merchants and buyers according to someimplementations.

FIG. 3 illustrates an example probabilistic model for associatingtransactions with buyer profiles according to some implementations.

FIG. 4 is a block diagram illustrating an example of merging buyerprofiles based on transaction information according to someimplementations.

FIG. 5 is a flow diagram illustrating an example process for associatingtransactions with buyer profiles according to some implementations.

FIG. 6 is a block diagram illustrating an example of determining itemsto recommend to merchants according to some implementations.

FIG. 7 illustrates an example interface for presenting itemrecommendations to a merchant according to some implementations.

FIG. 8 is a flow diagram illustrating an example process for providingitem recommendations to merchants according to some implementations.

FIG. 9 is a flow diagram illustrating an example process for providingitem recommendations to merchants according to some implementations.

FIG. 10 illustrates an example architecture of a payment and itemrecommendation system for providing a payment and item recommendationservice according to some implementations.

FIG. 11 illustrates select components of one or more example servicecomputing devices according to some implementations.

FIG. 12 illustrates select components of an example merchant deviceaccording to some implementations.

FIG. 13 illustrates select components of an example buyer deviceaccording to some implementations.

DETAILED DESCRIPTION

Some implementations described herein include techniques andarrangements for providing item recommendations to a merchant based onpoint of sale (POS) transaction information received from the merchantand/or from other merchants. For example, a service provider may providea payment and item recommendation service to merchants and buyers toenable transactions between the merchants and the buyers at POSlocations. As used herein, a transaction may include a financialtransaction for the acquisition of goods and/or services (referred toherein as items) that is conducted between a buyer (e.g., a customer)and a merchant, such as at a POS location. In some examples, during atransaction, or in anticipation of a transaction, the service providermay send an item recommendation to a merchant device associated with amerchant to recommend one or more items for the merchant to offer to abuyer, such as for cross-selling, up-selling, bundling, or the like.

In some instances, cross-selling may include inviting a customer tospend more money by adding an additional item to a first item that thecustomer has already selected for purchase. For instance, the added itemmay be from an item category that is different from that of the firstitem, but which might be considered an accompaniment to the first itemor related to the first item. As one example, if a customer has ordereda cup of coffee, the merchant may offer a donut with the coffee as across-sell suggestion. Similarly, up-selling may include inviting acustomer to spend more money on an item selected by the customer, suchas to buy a more expensive model of the same type of item, addingoptional features to the selected item, adding a warranty, and so forth.As one example, if the customer has selected a portable media player forpurchase, an up-sell would be to offer the customer the same brand or adifferent brand of media player that has more memory for a higher price.Bundling is similar to cross selling and up-selling, and may involveoffering two or more related items for sale together at a discountedprice, to thereby entice the buyer to consider purchasing the two ormore items together.

Furthermore, when paying for a transaction, a buyer can provide thepayment that is due to the merchant using any of cash, a check, apayment card, or an electronic payment account, such as may be accessedusing a buyer device carried by the buyer. The merchant can interactwith a POS computing device, i.e., a merchant device, to process thetransaction. During the transaction, the merchant device can send, tothe service provider, transaction information describing thetransaction, such as a description of the item(s) selected by the buyer,price(s) of the item(s) selected, a time, place and date of thetransaction, and so forth. In addition, the merchant device can ofteninclude buyer identifying information with the transaction informationsent to the service provider. For instance, buyer identifyinginformation may be determined from a payment card of the current buyer,from an electronic payment account of the current buyer, from a merchantclub membership for which the current buyer has signed up, or the like.

Accordingly, in some examples herein, when a buyer is participating in acurrent POS transaction with a merchant, the service provider mayreceive transaction information about the current POS transaction from amerchant device associated with the merchant. The received transactioninformation may identify an item selected by the buyer, and in somecases may include buyer identifying information. In response toreceiving the transaction information, the service provider maydetermine one or more additional items for the merchant to offer to thebuyer.

As one example, suppose that a buyer has selected a first item forpurchase, such as a cup of coffee. As the transaction is taking place,the service provider may receive the transaction information thatidentifies the buyer and the first item selected by the buyer as cup ofcoffee. Based at least in part on the received transaction information,the service provider may determine one or more additional items that themerchant can offer to the buyer and that the buyer may be likely topurchase. The service provider may send an item recommendation to themerchant regarding the one or more additional items. The itemrecommendation may be presented on the merchant device so that themerchant can offer the one or more additional items to the buyer priorto completion of the transaction.

In some instances, the one or more additional items may be targeted tothe particular buyer such that there is a greater than normal likelihoodthat the buyer will buy the one or more additional items with the firstitem in the same transaction. As an example, the service provider maydetermine from a transaction history in a buyer profile associated withthe particular buyer whether the particular buyer has bought any otheritems with the first item in the past. Further, the service provider mayrefer to a list of items offered by the merchant to determine whetherany of the items bought by the particular buyer with the first item inthe past are offered by the merchant participating in the currenttransaction. If so, the service provider may recommend one or more ofthe other items to the merchant as a cross-sell, up-sell or bundleopportunity.

As an example, suppose that the buyer's transaction history shows thatthe buyer has purchased a blueberry muffin with a cup of coffee at oneor more other merchants 11% of the time, and has purchased a glazeddonut with a cup of coffee at one or more other merchants 14% of thetime. The merchant's list of available items offered may show that themerchant offers blueberry muffins, but does not offer glazed donuts.Accordingly, the service provider may send an item recommendation to themerchant for presentation on the merchant device in near real time,e.g., before the transaction is completed. The item recommendation mayrecommend that the merchant offer to sell a blueberry muffin to thebuyer to accompany the cup of coffee the buyer has already selected.Further, since the comparison with the list of available items offeredfor sale by the merchant indicates that this particular merchant doesnot offer glazed donuts, the item recommendation may not include arecommendation for a glazed donut or other items that the merchant doesnot currently offer.

As another example, suppose that, from the current transactioninformation, the service provider is not able to identify a buyerprofile corresponding to the particular buyer participating in thecurrent transaction. For instance, the service provider may not have abuyer profile that matches buyer information received with thetransaction information for the current transaction, or alternatively,the buyer may be paying with cash and has not otherwise provided anybuyer information. In either case, when the service provider cannotdetermine a buyer profile corresponding to the buyer participating inthe current transaction, the service provider may refer to a transactionhistory for the merchant to determine one or more items that have soldwell with the selected item among the merchant's recent customers.Additionally or alternatively, the service provider may refer totransaction histories for similar merchants.

Based at least in part on the transaction history for the currentmerchant and/or transaction histories for similar merchants, the serviceprovider may send to the merchant device a recommendation that themerchant offer one or more additional items to the particular customerbased at least in part on the first item that the customer has alreadyselected. For example, suppose that a customer has selected nail polishas an item for purchase. The transaction history for the currentmerchant may be obtained from a plurality of buyer profiles of buyersthat have conducted at least one transaction with the merchant.Alternatively, the transaction history for the current merchant may beobtained directly from a merchant profile for the current merchantmaintained by the service provider. Still alternatively, transactionhistories for POS transactions at a plurality of merchants similar tothe current merchant, such as classified in the same category as thecurrent merchant, may be accessed to determine items bought togetherwith nail polish. Additionally, in some cases, other transactioninformation in the transaction histories may also be taken intoconsideration, such as date and time of day of the transactions, overallamount being spent, locations of the transactions, and so forth.

Suppose the transaction history for the current merchant and/or thetransaction histories for similar merchants show that buyers oftenpurchase nail polish remover and/or cotton balls when purchasing nailpolish. Consequently, the service provider may determine, e.g., from alist of items that the merchant offers for sale, that the first merchanthas nail polish remover and cotton balls available for sale. Based onthis determination, the service provider may send an item recommendationto the merchant device to recommend that the merchant offer at least oneof nail polish remover or cotton balls to the particular buyer as partof the transaction for the nail polish. Thus, the merchant device mayreceive, from the service provider, a list of one or more additionalitems offered by the merchant that have sold well (e.g., with more thana threshold frequency) with the item that the current customer hasalready selected. The merchant device may present this list on a displayassociated with the merchant device to enable the merchant to offer theone or more additional items to the buyer before the transaction iscompleted.

In some instances, rather than sending the item recommendation to themerchant device in near real time, e.g., before the transaction iscompleted, the service provider may determine in advance items for themerchant to offer together. For example, the predetermined itemrecommendations may be stored on the merchant device or at a storagelocation accessible to the merchant device. Further, in some cases, thepredetermined item recommendations that are provided in advance may beused to offer bundles, i.e., two or more items offered for sale togetherat a discount if the buyer purchases all of the items in the bundle in asingle transaction. Accordingly, the item recommendations may bepresented on the merchant device by a merchant application withouthaving to send transaction information to the service provider, whichmay decrease the response time for presentation of the itemrecommendations to the merchant as each transaction is being conductedand/or which may enable the merchant device to present recommendationswhen operating in an offline mode. The predetermined itemrecommendations may be periodically updated by the service providerbased on newly received transaction information received from theparticular merchant and/or from other merchants.

The service provider may continually receive transaction informationfrom a plurality of merchants, and may organize the transactioninformation into merchant profiles and buyer profiles. For example, asingle buyer profile may include transaction information for a pluralitytransactions conducted by the corresponding buyer with various differentmerchants. The transaction information from a plurality of buyerprofiles can be aggregated and analyzed to determine characteristics ofbuyers that are customers or potential customers of a particularmerchant, and to determine which items particular buyers or types ofbuyers have purchased together, or may be likely to purchase togetherduring a single transaction. The service provider may provide itemrecommendations to a merchant that are targeted or otherwisepersonalized for a particular buyer or type of buyer based on anexamination of the transaction information in the buyer profiles. Insome cases, the item recommendations may be made in near real time for atransaction currently taking place at the merchant's POS location. Inother cases, the item recommendations may be provided to the merchant inadvance of a transaction.

For discussion purposes, some example implementations are described inthe environment of a service computing device that makes targeted itemrecommendations to merchants based on analysis of transactioninformation and/or buyer profiles. However, implementations herein arenot limited to the particular examples provided, and may be extended toother environments, other system architectures, other types ofmerchants, and so forth, as will be apparent to those of skill in theart in light of the disclosure herein.

FIG. 1 illustrates an example environment 100 for a payment and itemrecommendation service according to some implementations. For instance,the environment 100 may enable a service provider to provide itemrecommendations to a merchant to assist the merchant in sellingadditional items to a buyer. In the illustrated example, one or moreservice computing devices 102 of the service provider are able tocommunicate with one or more merchant devices 104 over one or morenetworks 106. Further, each merchant device 104 may be associated with amerchant 108. For example, one or more first merchant devices 104(1) maybe associated with a first merchant 108(1). Further, other merchantdevices 104(2)-104(N) may be associated with other merchants108(2)-108(N). Each merchant device 104 may include an instance of amerchant application 110 that executes on a respective merchant device104. The merchant application 110 may provide POS functionality to themerchant device 104 to enable the merchant 108 to accept payments at aPOS location from one or more buyers 114. For example, the firstmerchant 108(1) may use a first merchant device 104(1) to acceptpayments at a first POS location 112 from a buyer 114. In some types ofbusinesses, the POS location 112 may correspond to a store or otherplace of business of the merchant, and thus, may be a fixed locationthat typically does not change on a day-to-day basis. In other types ofbusinesses, however, the POS location 112 may change from time to time,such as in the case that the merchant operates a food truck, is a streetvendor, a cab driver, etc., or has an otherwise mobile business, e.g.,in the case of merchants who sell items at buyer's homes, places ofbusiness, and so forth.

As used herein, a merchant may include any business engaged in theoffering of goods or services for acquisition by buyers in exchange forcompensation received from the buyers. Actions attributed to a merchantherein may include actions performed by employees or other agents of themerchant and, thus, no distinction is made herein between merchants andtheir employees unless specifically discussed. In addition, as usedherein, a buyer may include any entity that acquires goods or servicesfrom a merchant, such as by purchasing, renting, leasing, borrowing,licensing, or the like. Hereinafter, goods and/or services offered bymerchants may be referred to as items. Thus, a merchant and a buyer mayinteract with each other to conduct a transaction in which the buyeracquires one or more items from a merchant, and in return, the buyerprovides payment to the merchant.

In this example, the buyer 114 has a buyer device 116 that may execute abuyer application 118. For instance, some buyers 114 may carry buyerdevices 116, such as smart phones, tablet computers, wearable computingdevices, or the like, as further enumerated elsewhere herein, and someof these buyer devices 116 may have installed thereon the buyerapplication 118. The buyer application 118 may include electronicpayment capability, which enables the buyer 114 to make a payment to themerchant using the buyer application 118, rather than paying with aphysical payment card, cash, check, etc. The buyer application 118 mayfurther enable the buyer 114 to check in with the merchant, e.g., at themerchant's store or prior to entering the merchant's store, such as toplace an order for an item. As one example, the buyer 114 may be able toplace an order for an item through the buyer application 118, may skipwaiting in a line for ordering items, may pay for the transaction usingthe buyer application 118, and may proceed directly to an area of themerchant's store to pick up the ordered item.

In the example of FIG. 1, suppose that the buyer 114 is conducting atransaction with the merchant 108(1) to purchase an item at the POSlocation 112. The merchant application 110 on the merchant device 104(1)may send first transaction information 120(1) to the service computingdevice 102, and typically, may send the first transaction information120(1) as the transaction is being conducted at the point of salelocation 112. For instance, the transaction information 120 may be sentby each of the merchant devices 104 as each transaction is conducted. Ofcourse, in other examples, such as if the merchant is processingtransactions offline, the transaction information 120 may be sent in abatch at a subsequent point in time or using other suitable techniques.

The transaction information 120 may include information regarding thetime, place, and the amount of the transaction, information related tothe item acquired, a type of payment being used (e.g., cash, check,payment card, electronic payment), as well as additional information,such as buyer information. For instance if a payment card is used, thetransaction information 120 can include data stored in the payment card,e.g., Track 1 data (cardholder name, card number and other cardinformation). In addition, when completing the transaction a buyer maysometimes provide a receipt email address for receiving a receiptthrough email. Other examples of transaction information 120 that can becaptured include item information, e.g., an itemized listing of theitems being acquired, the price being paid for each item, descriptors ofthe items (size, flavor, color, etc.), geolocation data indicating ageographic POS location of a particular transaction, online/offline carddata, data describing the merchant, e.g., a merchant identifier, amerchant category code (MCC), any type of data that is received upon abuyer's authentication into a social network, if any, and various othertypes of information, as discussed additionally below.

The service computing device 102 may also receive transactioninformation 120 from the plurality of other merchants 108. For example,a large number of other merchants 108(2)-108(N) may also operate theirown merchant devices 104(2)-104(N), respectively, for conductingtransactions with respect to their own businesses. Accordingly,transaction information 120(2)-120(N) from the merchant devices104(2)-104(N) associated with the other merchants 108(2)-108(N) may alsobe provided to the service computing device 102.

As discussed additionally below, the service computing device 102 mayreceive the transaction information 120 and may associate thetransaction information 120 with merchant information 122 maintained bythe service computing device 102. For example, the first transactioninformation 120(1) may be associated with a first merchant profile124(1) corresponding to the first merchant 108(1), the Nth transactioninformation 120(N) may be associated with an Nth merchant profile 124(N)corresponding to an Nth merchant 108(N), and so forth.

In addition, buyer information 126 may be extracted from the transactioninformation 120 and may be associated with buyer profiles 128. Forexample, the transaction information 120 for a particular transactionmay include a payment card identifier of the payment card that was usedas a payment instrument, and may further include the name of the holderof the payment card that was used. Accordingly, a buyer profile 128 maybe associated with an identifier of the payment card and/or the namecorresponding to the holder of the payment card. Additional transactioninformation may be related to this buyer profile, such as the POSlocation of the transaction, the amount of the transaction, the time anddate of the transaction, the item(s) acquired through the transaction,descriptive information about the item(s) acquired, the individual pricepaid for each item, and so forth.

The service computing device 102 may include an item recommendationmodule 130 that may analyze the merchant profiles 124 and/or the buyerprofiles 128 for making item recommendations 132 to one or more of themerchants 108. The item recommendations 132 may recommend items forcross-selling, up-selling and/or bundling. In some implementations, theitem recommendations 132 may be provided to the merchant device 104 innear real time, e.g., while the transaction is taking place. In otherexamples, the item recommendations 132 may be predetermined and providedin advance to the merchant device 104.

As one example, in response to the service computing device 102receiving transaction information 120, such as from a payment cardswipe, a buyer check in using an electronic payment option of the buyerapplication 118, or the like, the service computing device 102 may beable to identify the buyer 114 and may access a buyer profile 128associated with the buyer. The buyer profile 128 may include the buyer'stransaction history for transactions not only with the current merchant108(1), but also with other merchants, including merchants that arecategorized similarly to the current merchant 108(1). The servicecomputing device 102 can determine, from the transaction history in thebuyer's profile 128, one or more items that the merchant may offer asup-sell, cross-sell or bundle items. The service computing device 102may send one or more item recommendations 132 in an electroniccommunication 134 to the merchant device 104(1). For example, thecommunication 134 may include a list of the one or more items determinedby the service computing device 102 for the merchant 108(1) to recommendas an accompaniment to a selected item 136 already selected by the buyer114.

In some cases, the merchant device 104(1) that is being used to conductthe current POS transaction may present the one or more itemrecommendations 132 in a pop-up window or at a designated location in auser interface (UI) presented on a display 138 associated with themerchant device 104(1). As indicated above, the item recommendation 132may be based at least in part on information that the service provideralready has about the particular buyer 114 in the buyer profile andabout the selected item 136 that the particular buyer 114 is about topurchase. The item recommendations 132 may recommend one or morerecommended items 140 for the merchant 108(1) to offer to sell to thebuyer 114 and/or may recommend a bundle or incentive for the buyer 114to purchase one or more recommended items with the selected item 136already selected. For example, if the merchant 108 sells a first itemplus one or more second items together, even at a bundle discount, themerchant 108 can obtain larger overall revenue, which can typically leadto greater overall profitability for the merchant.

In the example of FIG. 1, suppose that the buyer 114 has selected item Aas the selected item 136. For instance, the buyer 114 may have used thebuyer application 118 to check in with the merchant 108(1) and to selectitem A from a menu of items offered by the merchant 108(1). As anotherexample, the buyer 114 may have entered the merchant's POS location 112,and may have picked up item A from a shelf, may have stood in a line toorder item A, may have ordered item A from a waitperson, server, etc.

Furthermore, in some cases, buyer information may also be determinedabout the buyer 114 as the transaction is being conducted. For example,if the buyer 114 uses the buyer application 118 to order item A, thebuyer 114 may be identified through a buyer profile associated with thebuyer application 118. As another example, if the buyer 114 uses apayment card, such as by swiping the payment card at the time ofcheckout or when opening a tab, the service computing device 102 mayreceive the information from the payment card, such as a payment cardidentifier based on the payment card number and the name of a holder ofthe payment card. As still another example, if the merchant provides aclub card or membership incentives, the buyer may provide identifyinginformation based on such a program, which information may already be onfile with the merchant, which may include identifying information, suchas a phone number, or the like.

Suppose that the transaction information 120(1) includes buyerinformation that enables the service computing device 102 to associatethe transaction information 120(1) with a particular buyer profile.Accordingly, the service computing device 102 may access a transactionhistory in the buyer profile to determine whether the particular buyer114 has ever purchased the selected item 136, or, in some examples, anitem in the same item category as the selected item 136. Suppose thetransaction history in the buyer profile 128 indicates that the buyer114 has purchased the selected item 136, i.e., item A, a number of timesfrom the merchant 108(1), and further, the buyer has purchased item Bwith item A in the same transaction 17% of the time at the merchant108(1). Further, suppose that the transaction history indicates that thebuyer 114 has purchased item B together with item A in the sametransaction 21% of the time when purchasing item A from other merchants.Accordingly, the service computing device 102 may send a firstrecommendation 142 to the merchant 108(1) to inform the merchant 108(1)of these findings and to recommend the merchant 108(1) offer item B tothe buyer 114.

In addition, the service computing device 102 may determine from thebuyer profiles 128 a subset of the buyer profiles that share one or morecommon characteristics with the buyer profile of the buyer 114. Forexample, the subset may be selected based on one or more commondemographic characteristics, such as: buyer residence information; buyerage; buyer sex; buyer affluence; buyer ethnicity; buyer language; buyereducation; buyer marital status; buyer occupation; buyer religion; buyerpolitical affiliation; buyer memberships in associations, and so forth.Additionally, or alternatively, the one or more common characteristicsmay be selected based on shopping habits, such as type of items that thebuyers purchase, a time of day and day of the week on which purchasestend to be made, categories of merchants at which the buyers shop,average amount spent per transaction, and so forth.

As one example, suppose that the service computing device 102determines, from the buyer profiles 128, a subset of the buyer profiles128 that have a plurality of the above-listed characteristics that aresimilar to the buyer profile of the buyer 114. Further, supposed thatcomparison of the buyer profiles in the subset with each other showsthat 22% of the buyers buy item C with item A when buying item A fromeither the merchant 108(1) or from other merchants. Accordingly, theservice computing device 102 may send a second recommendation 144 to themerchant 108(1) to inform the merchant 108(1) of these findings and torecommend the merchant 108(1) offer item C to the buyer 114. Uponreceiving the recommendations 1402 and 144, such as prior to completionof the transaction, the merchant 108(1) may ask the buyer 114 if thebuyer would like to add item B and/or item C to the transaction.

In some cases, the merchant device 104(1) may be a portable device, suchas may be carried by a waitperson or other server. For instance, in thecase that the merchant device 104(1) is used by a server in a restaurantfor taking customer orders, the item recommendations 132 may includerecommendations for accompaniments for particular menu items ordered bya buyer. As one example, the merchant device 104(1) may presentautomatic recommendations for wine pairings or appetizers when theserver enters a buyer's meal selection. As another example, if themerchant 108(1) desires to sell off unwanted inventory, the itemrecommendations 132 may indicate which buyers are likely to buy theparticular inventory, which are likely to buy if offered a special dealor other incentive, and so forth.

In some implementations, the service computing device 102 may providepredetermined recommendations 132 to the merchant devices 104. Forexample, if the merchant device 104(1) is operating in an offline mode,the predetermined recommendations may be provided by the merchantapplication 110 executing on the merchant device 104(1). For instance,the predetermined recommendations may indicate which of the itemsoffered by the merchant have been sold with each other with a frequencythat is larger than a threshold frequency. According, in response to thebuyer selecting item A, the merchant application 110 may present one ormore recommendations for items that have sold well with item A in thepast.

Additionally, in some examples, the communication 134 from the servicecomputing device 102 may include an indication to the merchant 108(1)about one or more items that the merchant 108(1) could also have sold tothis buyer 114, such as items not currently offered by the merchant108(1). Accordingly, item recommendations 132 may be provided to themerchants 108 and targeted to particular buyers 114 based on analysis ofthe buyer profiles 128 and/or the merchant profiles 124, additionalexamples of which are discussed below.

FIG. 2 illustrates an example environment 200 for enabling transactionsbetween merchants and buyers according to some implementations. In thisexample, a buyer 114 may use any of a variety of different paymentinstruments 202 when participating in a plurality of POS transactions204(1)-204(M) with a plurality of different merchants 108(1)-108(N). Forexample, a buyer may typically have a plurality of payment cards206(1)-206(L), such as credit cards, debit cards, prepaid cards, and soforth, that the buyer 114 may use for conducting various different POStransactions 204. Further, in some examples, the payment cards 206 mayinclude one or more magnetic strips for providing card and buyerinformation when swiped in a card reader. In other examples, other typesof payment cards 206 may be used, such as smart cards having a built-inmemory chip, a radiofrequency identification tag, and so forth.

A buyer 114 may select a particular payment card 206 for use at aparticular POS location and/or for use with a particular merchant 108for any of a variety of different reasons and may often use differentpayment cards. For example, the buyer 114 may not always use the samepayment card 206 with the same merchant 108 for every POS transaction204 conducted with that merchant 108. In such scenarios, the transactioninformation that describes transactions that are conducted using a firstpayment instruments 202 may be separate or disconnected from thetransaction information 120 that describes other transactions conductedusing a second payment instrument 202. Such disconnected sets oftransaction information 120 can make it difficult to capture a holisticview of a buyer's shopping behavior and preferences. Thus, in someexamples herein, various techniques may be used for matching transactioninformation corresponding to multiple different payment instruments to asingle buyer profile. This enables creation of a single or more completebuyer profile to describe the shopping behavior of the correspondingparticular buyer. Such buyer profiles 128 of respective buyers 114 canbe aggregated and the information contained therein evaluated to providemerchants with data describing their customer base, and to provide themerchants with customized or otherwise personalized item recommendationsand other types of information.

In addition to payment cards, a buyer 114 may carry a buyer device 116,as discussed above. The buyer device 116 may include the buyerapplication 118, which enables an associated electronic payment accountto be used as a payment instrument 202. For example, the buyerapplication 118 may include an electronic payment module 208 that usesan electronic payment account of the buyer 114 for making electronicpayments for transactions. In some cases, the electronic payment accountof the buyer 114 may be linked to one of the buyer's payment cards 206,such as a credit card. Accordingly, the buyer application 118 may enablethe buyer 114 to pay for a transaction with the linked credit cardwithout having to produce the credit card, thereby enabling a card-lesspayment to the merchant with the credit card. The buyer application 118and the corresponding electronic payment account, can be associated withvarious buyer information including, for example, the buyer's name,information describing the payment card linked to the electronic paymentaccount, and an email address linked to the electronic payment accountto which receipts can be sent for electronic payment transactions thatare conducted by the buyer 114 using the buyer application 118. Further,as an alternative to linking the electronic payment account to a creditcard, the electronic payment account may be a different type of account,such as a checking account, a debit account, a savings account, aprepaid account having a prepaid quantity of money deposited therein, orthe like.

In addition to the above discussed payment instruments, the buyer 114may also optionally pay with a check 210 or cash 212. For example, ifthe buyer 114 pays with check 210 or cash 212, the merchant maysometimes also receive an identifier 214 that provides additionalidentification information about the buyer 114. For instance, a merchantmay have a club card or other incentive that enables identification ofthe buyer to the merchant and thereby to the merchant application 110.As an example, the buyer 114 may provide a telephone number associatedwith the buyer 114, and this telephone number along with othertransaction information may be cross-referenced to a matching telephonenumber in an existing buyer profile 128 to associate the transactionwith the existing buyer profile 128. Additionally, or alternatively, thebuyer 114 may provide an email address in association with a particulartransaction to receive a receipt for the transaction by email, ratherthan receiving a paper receipt, and the email address may be used toassociate the transaction with an existing buyer profile 128.Alternatively, if the buyer 114 pays with a check 210, the buyer 114 maybe required to provide buyer information in association with the check210, which, in addition to a checking account number, may includetelephone number, address, and other identification information.Accordingly, this information may also be associated with the particulartransaction, and may thereby enable the transaction to be associatedwith an existing buyer profile 128. Furthermore, as discussedadditionally below, if an existing buyer profile 128 that matches thetransaction information for a new transaction cannot be located, a newbuyer profile 128 may be created.

The service computing device 102 may include a payment processing module216 that may receive at least transaction information 120 for processingpayments made through the merchant application 110 and, in some cases,the buyer application 118. For example, the payment processing module216 may receive transaction information 120, such as an amount of thetransaction, and may verify that a particular payment card can be usedto pay for the transaction, such as by contacting a card clearinghousecomputing device or other bank computing device, as discussedadditionally below. Furthermore, in some examples, the paymentprocessing module 216 may redirect payment information for transactionsto be made using payment cards 206 to a bank computing device (not shownin FIG. 2), while in other examples, the merchant devices 104 maycommunicate directly with an appropriate bank computing device forapproving or denying a transaction using a particular payment card 206for a particular transaction. Additional details of payment processingare discussed below.

The service computing device 102 may further include the analysis module218 mentioned above. For example, the analysis module 218 may receivethe transaction information 120 and associate the transactioninformation 120 with appropriate merchant profiles 124 and appropriatebuyer profiles 128. Thus, as discussed additionally below, the analysismodule 218 may compare received transaction information 120, which mayinclude an identifier of the merchant device 104 or an identifier of aninstance of a merchant application 110 from which the transactioninformation 120 is received for associating the transaction information120 with a particular merchant profile 124. Furthermore, the analysismodule 218 may extract buyer information such as credit card identifier,buyer name, buyer email address, and various other pieces of buyerinformation from the transaction information 120, and may match thisinformation with an existing buyer profile 128. If no match is found,then a new buyer profile 128 may be created.

The buyer profiles 128 and/or merchant profiles 124 described herein maybe created and maintained using any suitable types of data structures,and using any suitable data storage or database techniques. In someexamples, the transaction information and other profile information maybe maintained in a relational database in which pieces of informationfor individual buyer profiles may be stored distinctly from one another,but are related to or otherwise associated with each other in therelational database. For instance, a particular buyer profile 128 may beobtained by generating a view of a portion the data related in thedatabase to the particular buyer profile, or by otherwise extracting thedata from the database. Alternatively, of course, other types of storagetechniques may be used for generating and maintaining the buyer profiles128 and/or the merchant profiles 124. Furthermore, as discussed below,in some examples a probabilistic model may be used to determine within athreshold level of confidence that transaction information 120 from aparticular transaction should be associated with a particular buyerprofile 128.

FIG. 3 illustrates an example probabilistic model 300 for associatingtransactions with buyer profiles according to some implementations. Inthis example, the probabilistic model 300 includes a weighted graph inwhich triangular nodes represent buyer profiles, such as a first buyerprofile 128(1) and a second buyer profile 128(2), respectively. Theprobabilistic model 300 indicates the probabilities that particulartransactions are associated with particular buyer profiles, such asbased on buyer use of different payment instruments, as discussed abovewith respect to FIG. 2, and further based on the information includedwith each transaction and the information already included in each buyerprofile 128. The probabilistic model 300 can be used to determinewhether to associate a particular transaction with a particular buyerprofile, and to determine when to merge buyer profiles 128 and/or toindicate when new buyer profiles should be created.

In the example of FIG. 3, the probabilistic model 300 shows that thetriangular node that represents the first buyer profile 128(1) isassociated with a square node 302, which represents a first payment cardwith a 1.0, i.e., 100 percent, confidence score, as indicated at 304.The confidence score 304 indicates that the financial transactionsconducted using the first payment card correspond to the first buyerprofile 128(1) with a confidence of 100 percent, but, of course, thisconfidence score may not necessarily authorize a buyer to use thecorresponding payment card, as such authorizations are managedseparately. Circular nodes 306 and 308 represent respective transactionsthat were conducted using the first payment card, and are therebyassociated with the node 304. Accordingly, the transaction informationcorresponding to the transactions 306 and 308 can be associated with thefirst buyer profile 128(1) with a confidence level of 100 percent.

The triangular node that represents the first buyer profile 128(1) isalso associated with a square node 306, which represents a secondpayment card with a 1.0 confidence score. For example, suppose that thefirst buyer associated a particular email address with the first buyerprofile 128(1), such as when signing up for an electronic paymentaccount. Subsequently, suppose that the first buyer used the secondpayment card for a transaction and requested that a receipt be sent tothe same email address as the email address associated with the firstbuyer profile. Accordingly, based on matching of the email addressassociated with the second card and the email address previouslyassociated with the first buyer profile 128(1), the second payment cardmay be associated with the first buyer profile with a 1.0 confidencescore, as indicated at 312. This confidence score 312 indicates thattransactions, such as a transaction represented by a circular node 314,conducted using the second payment card may be included in the firstbuyer profile 128(1) with a confidence level of 100 percent.

In addition, the model 300 shows the triangular node 302 that representsthe first buyer profile 128(1) and the second triangular node thatrepresents the second buyer profile 128(2) are both associated with asquare node 316, which represents a third payment card. For example,suppose that when the transaction information including the thirdpayment card information was received, an identifier associated with thethird payment card did not the match card identifiers in any currentbuyer profiles. Further, suppose that the name associated with the thirdpayment card (Fred T. Redd) is the same as the names associated with twobuyer profiles, i.e., the first buyer profile 128(1) and the secondbuyer profile 128(2). Accordingly, the third card may be associated withthe first buyer profile 128(1) and the second buyer profile 128(2) usinga 0.5, or 50 percent, level of confidence as the confidence score, asindicated at 318 and 320, respectively. Consequently, the model 300indicates that there is a 50 percent probability that a transactionrepresented by a circular node 322 conducted using the third paymentcard was performed by the first buyer associated with the first buyerprofile 128(1) and a 50 percent probability that the transaction wasconducted by the second buyer associated with the second buyer profile128(2).

Accordingly, in some examples, the transaction information for thetransaction represented by node 322 may not be associated with either ofthe first profile 128(1) or the second profile 128(2), since a name isnot always a reliable indicator of an individual identity. However, inother examples, other information included with the transactioninformation may be taken into consideration to change the confidencelevels 318, 320. For instance, the second buyer profile 128(2) may havea fourth payment card associated, as indicated by square node 324, witha confidence score of 1.0, as indicated at 326. Accordingly, atransaction corresponding to circular node 328 may be associated withthe second buyer profile 128(2) with 100 percent confidence.

As an example, suppose a comparison of the item purchase informationfrom the transaction associated with node 322 with the item purchaseinformation for the transactions associated with nodes 306, 308, 314 and328 indicates that the transaction associated with node 322 took placeat the same merchant POS location and at the same approximate time ofday, but on a different date, as the transaction associated with node308. Further, suppose that the transaction associated with node 328 andassociated with the second buyer profile 128(2) took place in adifferent city from the transactions associated with nodes 306, 308, 314and 322. Furthermore, suppose that the transaction associated with node308 is for a medium-sized vanilla latte and a blueberry bagel, and thatthe transaction associated with node 322 is for a medium-sized vanillalatte and a cinnamon bagel. Accordingly, in some instances, thetransaction information for the respective transactions may be used tosubstantially change the confidence scores 318 and 320, which, if theconfidence score exceeds a threshold, results in the transactioninformation associated with node 322 being associated with the firstbuyer profile 128(1).

As one example, probabilistic model 300 may include a trainedstatistical model that accounts for numerous pieces of informationincluded in the transaction information for various types oftransactions, such as location of the transaction, type or category ofmerchant, time of day of the transaction, day of the week, itemspurchased through the transaction, descriptors of items purchased,amount paid for the transaction, and so forth, in addition toinformation such as payment card identifier, name associated with thepayment card, and any other information, such as email addresses, homeor business addresses, phone numbers, etc. The statistical model may beinitially trained using a set of training data, checked for accuracy,and then used for matching transactions with particular buyer profilesby determining confidence scores, and associating a particulartransaction with a particular buyer profile when a confidence scoreexceeds a specified threshold of confidence. The statistical model maybe periodically updated and re-trained based on new training data tokeep the model up to date. Examples of suitable statistical models thatmay be incorporated into the one or more probabilistic models 300 hereinmay include regression models, such as linear regression models, andstochastic models, such as Markov models, hidden Markov models, and soforth.

For example, suppose that, based on analysis of the item purchaseinformation for the transactions associated with nodes 306, 308, 314,322 and 328, the confidence score 318 is greater than 0.8, while theconfidence score 320 is correspondingly less than 0.2. As one example,suppose that the threshold for associating transaction information witha buyer profile is 0.8. Then, if the probabilistic model 300 indicates aconfidence score that is greater than 0.8, the transaction informationassociated with the node 322 may be associated with the first buyerprofile 128(1). Thus, the buyer profiles 128 may indicate the itempurchase activity and payment activity of an associated buyer acrossmultiple payment accounts or other payment instruments.

Accordingly, the analysis module 218 may be configured to harmonize thetransaction information that is received from various merchant devicesso that orphan or otherwise disconnected sets of transaction informationthat correspond to different financial payment instruments, e.g.,different payment cards or electronic payment accounts, etc., can bematched to or otherwise associated with particular buyer profiles. Insome examples, the analysis module 218 is configured to match financialdata corresponding to different financial accounts using the one or moreprobabilistic models of buyer profiles and respective transactionsconducted using the different financial payment instruments. In someexamples, the analysis module 218 can apply the probabilistic model 300,for example, by utilizing one or more of a weighted graph model, aprobabilistic data store and/or a trained statistical model.

To generate and/or apply the probabilistic model 300, the analysismodule 218 may be configured to match, either exactly or heuristically,buyer information and/or item information included in the transactioninformation using one or more common characteristics. Characteristicsthat can be used to match transactions include a payment instrumentnumber, e.g., a debit card number or credit card number, Track 1 datafrom the payment card magnetic strip (e.g., a name of the buyer involvedin the transaction), an email address linked to the transaction (e.g., areceipt email address) or the name used by an buyer in an email usernamestring, e.g., “fred.redd@example.com,” to name a few examples. However,the techniques described herein can be performed using any type ofcharacteristic that can identify a buyer. Further, some characteristics,such as email address, phone number or payment card identifier, may havea higher level of confidence than other characteristics such as buyername.

Thus, the probabilistic model 300 may represent associations betweenbuyer profiles, respective financial accounts or other paymentinstruments, and the transactions associated with those accounts orother payment instruments. After finding a match between a buyer profileand transaction information for a particular transaction, theprobabilistic model 300 or the analysis module 218 can assign aconfidence score that is associated with that match. For example, sometypes of matches, such as email addresses, payment card identifiers,telephone numbers, and the like, may have such high confidence levelsthat a confidence score of 100 percent or 1.0 may be assigned if thereare no other matching buyer profiles. For heuristic matches, theconfidence score is a probability that represents a likelihood that aparticular transaction is associated with a particular buyer profile,rather than a different buyer profile. The analysis module 218 canupdate these probabilities as transaction information describing newtransactions is received from the merchant devices.

The analysis module 218 can use the probabilistic model to create or addto buyer profiles to provide a holistic view of a corresponding buyer'sshopping behavior and preferences, as compared to other buyers. Forexample, for a particular buyer, the analysis module 218 can determinebased on the buyer's history of transactions (using, for example, therespective itemized listing of purchases associated with thosetransactions) that the particular buyer is likely to prefer a vegetariandiet because the buyer orders less meat-based items than other similarbuyers. This probabilistic data point can then be added to theparticular buyer's buyer profile.

Some types of information can be associated with a buyer profile in aprobabilistic manner. For example, the buyer's gender and age may bedetermined within a certain confidence level based on the buyer's nameand third-party data, e.g., data from the U.S. Census Bureau, data froma social network site, data from a microblog site, or other onlinepresences of the various different buyers. The buyer's dietaryrestrictions or habits can be probabilistically determined from thebuyer's itemized listings of purchases. For example, if the buyer alwaysorders soy-based coffee, then a probabilistic data point indicating thatthis buyer is lactose intolerant may be included in the correspondingbuyer profile. Geographic locations corresponding to the buyer's homeand/or work locations can be probabilistically determined based on thegeographic locations of merchants where the buyer conducts transactions.For instance, the analysis module 218 can determine a likely home orwork location based on a geographic location at which a transaction wasconducted with a taxi and the corresponding amount charged by the taxi.The analysis module 218 can determine a radius beginning from thedrop-off geographic location and based on how far the taxi could travelfor the amount that was charged. Similarly, the analysis module 218 canadd probabilistic data points referencing the buyer's commutes tocertain geographic locations. For example, the analysis module 218 canprobabilistically determine the buyer's commutes to certain geographiclocations based on purchases made while on a predictable path the buyerfollows on certain days and at certain times.

Various hobbies and other activities can also be probabilisticallyassociated with the buyer profile of a particular buyer. For example,the analysis module 218 can evaluate the types of merchants at which thebuyer conducts transactions. The categories of these merchants can bedetermined, for example, using the merchants' self-declared businesscategory or using merchant category codes (MCC). The MCC is a four-digitnumber assigned to a business by credit card companies (e.g., AmericanExpress®, MasterCard®, VISA®) when the business first starts acceptingpayment cards as a form of payment. The MCC is used to classify thebusiness by the type of goods or services provided by the business.Accordingly, if the buyer is regularly shopping at a particular categoryof merchant, e.g., a bike shop, then the buyer can be probabilisticallyidentified as a cyclist. For example, if the buyer purchases bike lightsfrom a bike shop and returns to the bike shop the following week topurchase a spare tire, then a probabilistic data point indicating thatthe buyer is a cyclist might be added to the buyer's buyer profile.

As another example, the buyer's preferences for certain types ofclothing, shoes, sizes, and colors can also be determined from the iteminformation associated with the buyer's transactions. For example, ifthe buyer purchases red medium-sized shirts, then probabilistic datapoints indicating that the buyer prefers the color red, red shirts, andmedium-sized shirts, can be added to the corresponding buyer profile. Asdescribed below, buyer profiles for multiple buyers can be aggregated invarious ways to provide recommendations and other information tomerchants based on buyer characteristics.

FIG. 4 illustrates an example conceptual diagram 400 of merging buyerprofiles based on newly received transaction information according tosome implementations. In this example, as discussed above with respectto FIG. 3, the first buyer profile 128(1) and the second buyer profile128(2) are considered to be separate buyer profiles because the onlyconnection is that the same name (Fred T. Redd) is associated with bothbuyer profiles, which, taken alone, typically may not provide sufficientconfidence for merging two profiles. This example, illustrates a portionof the information that may be included in the first buyer profile128(1), including buyer first name 402, buyer last name 404, buyerstreet address 406, buyer city 408, buyer state 410, buyer postal code412, buyer phone number 414, buyer country 416, buyer first emailaddress 418, buyer second email address 420, and so forth, depending onthe information obtain from the buyer and/or obtained over time throughtransactions conducted by the buyer. The first buyer profile 128(1) mayfurther include electronic payment account information 422, which mayinclude transaction information for transactions conducted using theelectronic payment account associated with the first buyer profile128(1).

In addition, the first buyer profile 128(1) may further include anidentifier 424 for a first payment card associated with the first buyerprofile 128(1) and transaction information 426 for transactionsconducted using the first payment card. As one example, rather thanstoring an actual credit card number in association with a buyerprofile, a one-way hash function may be used to generate a cardidentifier, or various other encryption techniques may be used toprotect the security of the actual card information. Further, individualpayment cards may be individually distinguished by the information onthe card. For example, a single credit card number may be shared betweenspouses or other family members, but each card may have additionalinformation to distinguish one card from the other, and therebydistinguish a transaction conducted by a first family member from atransaction conducted by a second family member. In addition, the firstbuyer profile 128(1) may further include a payment card identifier 428for a second payment card associated with the first buyer profile 128(1)and transaction information 430 for transactions conducted using thesecond payment card.

In this example, and as a continuation of the example of FIG. 3discussed above, the second buyer profile 128(2) includes substantiallyless information than the first buyer profile 128(2). For instance, thesecond buyer profile 128(2) merely includes the buyer first name 432,buyer last name 434, a fourth payment card identifier 436, andtransaction information 438 for transactions conducted using the fourthpayment card.

Suppose that the service computing device has received new transactioninformation 440, which includes buyer information 442, such as the buyerfirst name 444, buyer last name 446, a buyer email address 448 to whicha receipt was sent, the fourth payment card identifier 450, and itempurchase information 452 for the new transaction 440 conducted using thefourth payment card. For example, the item purchase information 452 mayinclude a total amount of the transaction 454, a time and date of thetransaction 456, a location 458 of the transaction, such as ageolocation, street address, etc., a merchant identifier 460 of amerchant that participated in the transaction, identification of item(s)462 acquired by the buyer through the transaction, and may furtherinclude various other information related to the transaction (notshown), such as the price paid for each item, any descriptors associatedwith each item, such as color of the item, size of the item, flavor ofitem, and so forth.

Furthermore, suppose that the analysis module determines that the emailaddress 448 matches the buyer first email address 418 in the first buyerprofile 128(1) and that the fourth payment card identifier 450 matchesthe fourth payment card identifier 436 associated with the second buyerprofile 128(2). Consequently, as an email address and a payment card maytypically be considered identifiers of a high level of confidence, thenin some examples herein, the second buyer profile 128(2) may be mergedwith the first buyer profile 128(1), as indicated at 464. For example,to perform the profile merging 464, the information in the second buyerprofile 128(2), such as the fourth payment card identifier 436 and thetransaction information 438 may be related to or otherwise associatedwith the first buyer profile 128(1), and the second buyer profile 128(2)may be deleted, marked for deletion, marked inactive, or the like.

FIG. 5 is a flow diagram 500 illustrating an example process forassociating transactions with buyer profiles according to someimplementations. The process of FIG. 5 and the processes of FIGS. 8 and9 below are illustrated as collections of blocks in logical flowdiagrams, which represent a sequence of operations, some or all of whichcan be implemented in hardware, software or a combination thereof. Inthe context of software, the blocks may represent computer-executableinstructions stored on one or more computer-readable media that, whenexecuted by one or more processors, program the processors to performthe recited operations. Generally, computer-executable instructionsinclude routines, programs, objects, components, data structures and thelike that perform particular functions or implement particular datatypes. The order in which the blocks are described should not beconstrued as a limitation. Any number of the described blocks can becombined in any order and/or in parallel to implement the process, oralternative processes, and not all of the blocks need be executed. Fordiscussion purposes, the processes are described with reference to theenvironments, architectures and systems described in the examplesherein, although the processes may be implemented in a wide variety ofother environments, architectures and systems. Accordingly, in someimplementations, the example process 500 of FIG. 5 may be executed byone or more processors of the service computing device 102 of theservice provider.

At 502, the one or more computing devices may receive POS transactioninformation from a merchant device associated with a merchant. Forexample, as discussed above with respect to FIGS. 1 and 2, a pluralityof the merchant devices associated with a plurality of differentmerchants may send transaction information for a plurality oftransactions to the service computing device 102. Each instance oftransaction information may include various amounts of buyer informationand item purchase information, such as discussed above with respect toFIGS. 3 and 4.

At 504, the one or more computing devices may compare the transactioninformation across multiple buyer profiles to determine possible matcheswith existing buyer profiles. For example, as discussed above withrespect to FIGS. 3 and 4, buyer information from the transactioninformation may be compared with buyer information associated withexisting buyer profiles.

At 506, the one or more computing devices may determine whether there isa match of high confidence with a single buyer profile, such as throughmatching payment card identifiers, email addresses, telephone numbers,payment account identifiers, a merchant incentive program identifier, orother identifiers of high confidence. For example, the confidence of amatch for these types of identifiers may be sufficiently high thatadditional comparisons may not be required. However, in other examples,additional comparison may be performed as discussed below, such as toguard against the possibility that an incorrect phone number or emailaddress was entered, fraudulent use of payment cards, and so forth.

At 508, when there is a match of high confidence with a singleparticular buyer profile, the one or more computing devices may relateor otherwise associate the transaction information to the matching buyerprofile. For example, in the case of a relational database, thetransaction information may be related in the database to the buyerprofile. In other types of storage systems, the transaction informationmay be stored with a buyer profile, or may be otherwise associated withthe buyer profile using any suitable techniques.

At 510, when there is not a high confidence match with a particularbuyer profile, the one or more computing devices may apply one or moreprobabilistic models to determine probabilities, such as confidencescores, for relating the particular transaction information withparticular buyer profiles. For instance, as discussed above with respectto FIGS. 3 and 4, the one or more probabilistic models may include oneor more trained statistical models that take into consideration numerousdifferent aspects of the item purchase information such as time, date,place, merchant, items purchased, information about items purchased, andso forth, as well as the buyer information associated with thetransaction, as discussed above.

At 512, the one or more computing devices may determine whether athreshold level of confidence is met for associating the transactioninformation with any of the existing buyer profiles.

At 514, if the threshold level of confidence is not met for any of theexisting buyer profiles, the one or more computing devices may associatethe transaction information with a new buyer profile. For example, thebuyer information included with the transaction information may be usedto generate a new buyer profile and the transaction information may beassociated with the new buyer profile.

At 516, if the threshold level of confidence is met for at least onebuyer profile, the transaction information may be associated with thatbuyer profile.

At 518, the one or more computing devices may determine whether athreshold level of confidence is met for more than one profile. Forexample, as discussed above with respect to FIG. 4, the transactioninformation may include a first piece of buyer information, such as apayment card identifier, that can associate the transaction informationwith a high-level confidence to a first buyer profile, and may alsoinclude another piece of buyer information such as an email address,phone number, or the like, that can associate the transactioninformation with a second buyer profile.

At 520, if the transaction information can be associated with multiplebuyer profiles with a high-level of confidence that exceeds a secondthreshold, the first buyer profile and the second buyer profile may bemerged together as discussed above with respect to FIG. 4. In someexamples, the second threshold level of confidence may be different fromthe first threshold level of confidence. Further, other informationincluded in the multiple buyer profiles may be examined to determinethat there is not a conflict to the decision to merge the multiple buyerprofiles. The process may subsequently begin processing the nexttransaction received from the merchant devices.

FIG. 6 is a conceptual diagram 600 illustrating an example ofdetermining item recommendations for merchants according to someimplementations. As mentioned above, the item recommendation module 130can determine one or more items to recommend to a merchant as across-sell or up-sell opportunity based on received POS transactioninformation. In the example of FIG. 6, suppose that the itemrecommendation module 130 receives current transaction information 602for a transaction currently taking place between a first buyer and afirst merchant (not shown in FIG. 6). The item recommendation module 130generates one or more item recommendations to send to the first merchantbased at least in part on the current transaction information 602.

In this example, the current transaction information 602 includes firstbuyer information 604, item information 606 for one or more itemsselected by the first buyer, other transaction information 608, andfirst merchant information 610 about the first merchant. For example,the first buyer information 604 may include buyer identifyinginformation to enable the current transaction information 602 to bematched with an existing buyer profile 128. As mentioned above, examplesof buyer identifying information may include a payment card identifier,an electronic payment account (e.g., based on the first buyer's use ofthe buyer application for conducting the transaction), an email address,a phone number, a merchant club account of which the first buyer is amember, or the like.

Furthermore, the selected item information 606 may be for any itemselected by the first buyer, such as for any good or service offered bythe first merchant. For example, the first merchant (or an employeethereof) may enter the buyer's selection into the merchant device 104,such as by selecting an icon representative of the item, scanning abarcode associated with the item, or using any of other varioustechniques. As another alternative, the first buyer may use the buyerapplication 118 to select one or more items, the buyer application 118may transmit the selected item information to the service computingdevice 102, and the selected item information 606 may be provided to themerchant device by the service computing device 102. As still anotheralternative, the first buyer may use the buyer application 118 to selectthe item, and the buyer application 118 may communicate directly withthe merchant device to provide the selected item information. Inaddition to including an identification of the selected item, theselected item information 606 may include a price of the selected item,one or more descriptors of the selected item, such as color, size,style, flavor, etc., depending on the type of the selected item, and/orany other available information about the selected item.

The current transaction information 602 may further include the othertransaction information 608, such as the time of day, day of the week,and date on which the POS transaction is being conducted, the locationat which the POS transaction is being conducted, and/or any otherconditions under which the transaction is being conducted that may beconveyed through an electronic communication. Furthermore, the firstmerchant information 610 may include an identifier of the firstmerchant, which may be any suitable type of identifier, such as anidentifier assigned by the service provider, an identifier selected byor provided by the first merchant when signing up for the paymentservice, the name of the first merchant, an email address associatedwith the first merchant, an identifier associated with the merchantdevice of the first merchant, an identifier associated with an instanceof the merchant application executing on the merchant device, a GPSlocation of the merchant device, or any other suitable identifier thatcan be used to match the current transaction information 602 with afirst merchant profile 124(1) for the first merchant. Accordingly, thefirst merchant information 610 may be used to determine the firstmerchant profile 124(1) from the plurality of merchant profiles 124.

Similarly, the first buyer information 604 may be used to determine,from the plurality of buyer profiles 128, a first buyer profile 128(1)that corresponds to the first buyer participating in the current POStransaction. As discussed above, in some cases, the first buyerinformation 604 may be incomplete in that it is not possible todetermine with 100% confidence a particular buyer profile 128corresponding to the first buyer. In such a situation, a probabilisticmodel 612 may be applied as discussed above with respect to FIGS. 3-5for determining a buyer profile with which to associate the currenttransaction information 602. For example, if the first buyer information604 indicates an association with multiple buyer profiles 128, theprobabilistic model 612 may be used to associate the current transactioninformation 602 with a particular one of the buyer profiles 128 with alevel of confidence that exceeds a threshold level of confidence.

In either event, when a first buyer profile 128(1) has been determinedto correspond to the first buyer participating in the currenttransaction, the item recommendation module 130 may access at least oneof the transaction history 614 at other merchants included in the buyerprofile 128(1), or the transaction history 616 at the first merchantincluded in the buyer profile 128(1). The transaction historyinformation 614 and/or 616 may be provided to, or accessed by, itemrecommendation logic 618 that may use this information along with otherinformation when determining an item recommendation 620 for the firstmerchant. For example, the item recommendation logic 618 may be one ormore algorithms, computational models, or the like, configured todetermine particular items to recommend for cross-selling, up-selling,or bundling for generating the one or more recommendations 620.

As one example, the item recommendation logic may determine the selecteditem from the selected item information 606 and may compare the selecteditem with the transaction history at the first merchant 616 and/or thetransaction history other merchant 614 to determine which other itemshave been most frequently purchased with the selected item in pasttransactions. The item recommendation logic 618 may also access theitems 622 offered by the first merchant, when determining which items torecommend to the first merchant. For example, the first merchant mayprovide a menu or other listing of items offered by the merchant whensigning up for the payment service, or may otherwise provide a list ofitems in the first merchant's inventory, etc., to the service providercomputing device 102. Further, it is typically more useful to recommendthat the first merchant offer an item that is currently available fromthe first merchant when providing cross-sell or up-sell recommendationsto the first merchant, and therefore, the item recommendations may belimited to the items offered by the first merchant. However, in somecases, the item recommendation module 130 may determine that one or moreitems not available from the first merchant are often purchased in thesame transaction with the item currently selected by the first buyer. Insuch a situation, the item recommendation module 130 may send, e.g., ata later time, a communication to the first merchant recommending thatthe first merchant consider stocking or otherwise offering theparticular recommended item not currently offered, which may therebyincrease the overall profitability of the first merchant.

As another example, the item recommendation module 130 may determine asubset 624 of buyer profiles from the plurality of buyer profiles 128,based on at least one of similarity of characteristics with the firstbuyer profile 128(1), and/or based on other characteristics, such ashaving previously purchased the selected item from other merchants,and/or having previously purchased the selected item from the firstmerchant. For example, suppose the first buyer has never purchased theselected item before, or has never purchased another item with theselected item. Consequently, the item recommendation logic 618 may checkto see what buyers similar to the first buyer most frequently purchasewith the selected item. Thus, as one example, the item recommendationmodule 130 may search the buyer profiles 128, including the respectivetransaction histories 614 at other merchants or the respectivetransaction histories 616 at the first merchant to determine a subset ofbuyer profiles in which the transaction histories show a purchase of theselected item and one or more other items in the same transaction ateither the first merchant or at another merchant. For example, the itemrecommendation logic 618 may determine a subset of buyer profiles 624that are similar to first buyer profile based on one or more sharedcharacteristics, and may determine the item to recommend based at leastin part on a frequency of the item being purchased with the first itemamong the buyer profiles in the subset 624. In other words, therecommendation module 130 determines what item other buyers similar tothe current first buyer have purchased most frequently with the selecteditem, and recommends that item to the merchant.

In some examples, the transaction histories 614 for transactions atother merchants may be further filtered or limited to merchantscategorized in the same category as the first merchant or otherwisesimilar to the first merchant. The item recommendation module 130 mayaccess the first merchant profile 124(1) to determine informationrelevant to the first merchant, such as a merchant category, merchantlocation information, or various other types of merchant information,such as items offered for sale, hours of operation, and so forth. Forinstance, the MCC for a merchant or other classification techniques maybe used to categorize similar types of merchants into merchantcategories. In some examples, the merchant categories used herein do notmatch the MCC categories, but may be more inclusive or less inclusivecategories. Similarly, the merchants (and buyers) may be classified intolocation categories, such as for particular categories of geographicregions, e.g., same street, same neighborhood, same postal code, samedistrict of a city, same city, and so forth. Alternatively, of course,other location-based techniques may be used for determining merchantsand/or buyers in the same geographic region or within proximity to oneanother, etc., such as radial distance from a reference location, or thelike.

Furthermore, as mentioned above, the subset of buyer profiles 624 may befiltered or otherwise limited based on similarity of characteristicswith the first buyer profile 128(1). These characteristics may includebuyer characteristics and/or item purchase characteristics. For example,the first buyer profile 128(1) may include information about the firstbuyer, such as: buyer residence information; buyer age; buyer sex; buyeraffluence; buyer ethnicity; buyer language; buyer education; buyermarital status; buyer occupation; buyer religion; buyer politicalaffiliation; buyer memberships in associations, and so forth. Forinstance, the item recommendation module 130 may determine that 34% ofbuyers in the same age range and level of affluence purchased item Bwith item A from the first merchant, whereas only 10% of buyers overallmay have purchased item B when purchasing item A.

As one example, subsets can be determined using generally knownclustering techniques to group a plurality of buyer profiles in such away that buyer profiles that share certain characteristics are moresimilar to each other than other buyer profiles. For example, the itemrecommendation module 130 can create subsets of buyer profiles based onvarious behavioral characteristics or demographic characteristicsincluding, for example, geographic locations where the buyers shop, thecategories of merchants at which the respective buyers shop, the itemspurchased by the buyers, the time of day the buyers shop, the averageamount spent by the buyers in certain merchant categories, and so forth.

The item recommendation logic 618 may further take into considerationother transaction information 608, such as time of day and days of theweek during which items were acquired in the same transaction. Forexample, if the current transaction is taking place at 3:00 pm, the itemrecommendation 620 may be different from if the current transaction istaking place at 7:00 am in the morning. For instance, more buyers maytend to purchase a bagel with a cup of coffee in the morning, and morebuyers may purchase a cookie with the cup of coffee in the afternoon.The item recommendation logic 618 may further take into considerationother item information, such as descriptors related to the itemsacquired, prices paid for the items acquired, and so forth.

Furthermore, the first buyer profile 128(1) may provide an indication asto whether the first buyer is receptive to incentive offers, such asbeing more willing to purchase additional items if offered at a discountor as part of a bundle, such as based on whether the first buyer hasaccepted such deals in the past. As one example, if such is the case,the item recommendation module 130 may recommend that the first merchantoffer the second item with the selected item as a bundle at a discountto the first buyer. For instance, the item recommendation module 130 maysend a recommendation to the first merchant that identifies a seconditem to offer with the selected item, and may further recommend that thefirst merchant offer the second item with the first item as a bundle ate.g., a 5% discount on the overall combined price for the two items, ifpurchased together during the transaction.

As another example, suppose that the first buyer information 604 isinsufficient to identify a buyer profile 128 to associate with thecurrent transaction information 602. Accordingly, the itemrecommendation logic 618 may access a transaction history of the firstmerchant 626 to determine one or more items that have sold well in thepast with the selected item selected by the first buyer for the currenttransaction. For example, suppose that the first buyer is paying cashfor the selected item, and has provided no additional buyer information604. Accordingly, the item recommendation logic 618 may access atransaction history of the first merchant and/or other merchantsclassified in the same category as a first merchant to determine one ormore other items that are commonly sold with the first item selected bythe first buyer. Therefore, the item recommendation logic 618 may selectone or more of these items as recommended items to include in the itemrecommendations 620 for the first merchant. Consequently, while therecommendations are not targeted to the particular first buyer for thecurrent transaction, the recommendations may still be based on thoseitems preferred by the largest percentage of other buyers who frequentthe first merchant and/or other merchants similar to the first merchant,thereby maximizing the likelihood of the up-sell or cross-sell offerbeing accepted.

Furthermore, in addition to, or as an alternative to, using the merchantprofiles to obtain this information, the item recommendation logic 618may use the buyer profiles 128 to determine similar information. Forexample, the item recommendation logic 618 may search for transactionsin the buyer profiles 128 for the first item selected by the firstbuyer, and may determine additional items purchased with the selecteditem in the same transaction. The item recommendation logic 618 may thendetermine which items are purchased most frequently with the selecteditem. The item recommendation logic 618 may further apply additionalfilters, such as limiting the results to those merchants that aresimilar to the first merchant, limiting the results to transactionstaking place at approximately the same time of day, on the same day ofthe week, or the like.

In addition, in some examples the item recommendation module 130 mayprovide the item recommendations 620 to the first merchant in advance ofa transaction. For example, as discussed above, the item recommendationmodule may determine item recommendations for particular items 622offered by the first merchant. Accordingly, the item recommendationmodule 130 may make these recommendations in advance as predeterminedrecommendations, and the merchant application 110 on the merchant device104 may download these predetermined recommendations to the merchantdevice 104 for a particular merchant. For example, the predeterminedrecommendations might not be targeted to any particular buyer, butinstead may reflect which items offered by the particular merchant aremost frequently sold with which other items also offered by theparticular merchant. As another example, the predeterminedrecommendations may be filtered based on characteristics of buyers thatmost frequently purchase items from the particular merchant, to therebyprovide predetermined item recommendations that are somewhat targetedtowards buyers most likely to be purchasing items from the particularmerchant. The predetermined item recommendations may be further filteredbased on time of day, day of the week, overall transaction amount andnumerous other characteristics of transactions that have been conductedwith the least one of the first merchant or with other merchants thatare similar to the first merchant.

As mentioned above, the predetermined item recommendations may be storedon a local memory of the merchant device such as in the case that themerchant device is operated in an offline mode, such as by beingoperated out of contact with a network, or the like, or being otherwiseunable to communicate with the service computing device 102. As anotherexample, the predetermined item recommendations may be stored in thecloud or at the remote storage location that is accessible by theparticular merchant device 104. Furthermore, the item recommendationmodule 130 may periodically send updated predetermined recommendationsto the particular merchant such as when the particular merchant changesthe items 622 offered by the particular merchant, or when thepredetermined recommendations otherwise change due to changes in buyerpurchase habits or the like. In addition, the merchant may be able toaccess the predetermined recommendations such as through a dashboardmodule of the merchant application, or the like, to determine items thatthe merchant may wish to offer as a bundle so that the pricing of suchbundles can be determined by the merchant in advance.

FIG. 7 illustrates an example graphical user interface 700 forpresenting item recommendations to a merchant according to someimplementations. For example, one or more item recommendations 702 maybe presented in a window 704 or other area of the display 138 associatedwith the merchant device 104 (not shown in FIG. 7). Alternatively, ofcourse, the item recommendations 702 may be presented to the merchantusing any other suitable communication technology or presentationtechniques, such as audio presentation, presentation at a designatedarea of the UI 700, presentation on a separate display, and so forth.

In the example of FIG. 7, the recommendations 702 are shown as beingpresented in a window 704, such as a popup window, that may be closed bythe merchant after viewing, or which may close automatically after theelapse of a predetermined period of time. Additionally, in otherexamples, the recommendations 702 may be presented in any other suitabletype of window, or using any suitable type of graphic, overlaid text, orthe like. For instance, the window 704 may be presented as an overlay ona payment interface 706 that is used by the merchant for processingpayments from buyers at a POS location. In the illustrated example, thepayment interface 706 includes a plurality of icons 708, each of whichmay represent a separate item that is offered by the merchant, and aselected items/pricing column 710, which may show the item(s) selectedand a corresponding current transaction total. In some examples, thepayment interface 706 may be optimized to receive touch inputs from auser.

In the illustrated example, suppose that icon 708(A) has been selected,and is highlighted to indicate the selection of item A (not shown inFIG. 7), based on the buyer's selection of item A. Furthermore, supposethat the buyer has provided a payment card, which the merchant hasswiped, or that the buyer is using a payment account through the buyerapplication, or that the buyer has otherwise provided buyer identifyinginformation to the merchant. Item selection information and the buyeridentifying information may be sent to the service provider and, inresponse, the service provider may send back the one or more itemrecommendations 702, which may be presented in the window 704.

As an example, a first recommendation 712 may indicate that the currentbuyer purchases item B with item A from the current merchant 17% of thetime that the buyer purchases item A from the current merchant.Accordingly, recommendation 712 may recommend that the current merchantoffer item A to the current buyer. As another example, a secondrecommendation 714 may indicate that other buyers similar to the currentbuyer purchase item C with item A 21% of the time at other merchantssimilar to the current merchant. For example, the other merchants may beclassified in a same category as the current merchant, may be in asimilar geographic region category, etc., as discussed above.Furthermore, the current buyer may be classified in a same category as asubset of other buyers that share particular characteristics with thecurrent buyer. Consequently, the recommendation 714 may recommend thatthe merchant offer item C to the current buyer as well.

As another example, the third recommendation 716 may indicate, from acheck of the transaction history for the current buyer, that the currentbuyer has purchased bundles recommended to the current buyer in thepast. Accordingly, the third recommendation 716 may recommend that thecurrent merchant offer a bundle to the current buyer that includes bothitem B and item C with item A for a 10% discount off the total price ofitems A, B and C. Further, numerous other types of recommendations maybe provided to a merchant with the foregoing going being merely severalexamples for discussion.

Furthermore, the recommendations may be indicated to the merchant in anysuitable manner using any suitable interfaces, with the foregoing beingmerely several examples provided for descriptive purposes, and thus,recommendations herein are not limited to text-based recommendations.For instance, one of the icons 708 corresponding to a recommended itemmaybe highlighted or otherwise visually distinguished in the paymentinterface 706 to indicate to the merchant that the corresponding item isrecommended with the selected item, in addition to, or as an alternativeto the text-based recommendations. Further, highlighting the iconcorresponding to a recommended item may assist the merchant in selectingthe icon from the purchase interface 706 if the buyer agrees to add therecommended item to the transaction. As another example, if a bundle isrecommended, a new bundle icon (not shown) may be added to the paymentinterface 706 and presented temporarily (or permanently) in the paymentinterface 706, along with the discounted price of the bundle, to assistthe merchant in quickly recommending the bundle and adding the bundle tothe transaction if the buyer agrees. Numerous other variations will beapparent to those of skill in the art having the benefit of thedisclosure herein.

FIG. 8 is a flow diagram illustrating an example process 800 forproviding recommendations to merchants according to someimplementations. In some examples, the process may be executed by theservice computing device 102, or by other suitable computing devices.

At 802, the computing device receives transaction information from aplurality of merchant devices associated with a plurality of differentPOS locations. For example, as discussed above, the service providercomputing device may receive transaction information from a plurality ofdifferent merchant devices associated with a plurality of differentmerchants for POS transactions conducted between a plurality of buyersand the plurality of merchants.

At 804, the computing device may associate the transaction informationwith respective buyer profiles. For example, as discussed above withrespect to FIGS. 3-5, a probabilistic model or other suitable techniquesmay be used for associating the transaction information with respectivebuyer profiles.

At 806, the computing device may receive, from a first merchant deviceassociated with a first merchant, an indication of a first item selectedby a buyer for a current POS transaction. For example, the indication ofthe first item may be received from the merchant device being used toconduct a current transaction, and may further include buyer informationabout the buyer, when available.

At 808, the computing device may determine, from the transactioninformation for the plurality of transactions, one or more items thathave been acquired together with the first item in the past. Forexample, the computing device may access buyer profiles and/or merchantprofiles for determining which items the current buyer is likely topurchase with the currently selected item.

At 810, the computing device may compare the one or more items with alisting of items offered by the first merchant. For example, the firstmerchant may provide the service provider computing device with a menu,inventory, or other type of listing of items offered by the firstmerchant such as when the first merchant signs up for the paymentservice and/or when the items offered by the first merchant changes.Additionally, in some examples, the first merchant may provide a list ofone or more items that the first merchant would like to sell offquickly, such as stale inventory, or the like.

At 812, the computing device may determine a second item to recommendfor inclusion with the first item in the current transaction. Forexample, the second item may be an item that is offered by the firstmerchant, as determined based on the list of items offered by the firstmerchant, and may be an item that the current buyer has purchased withthe first item in the past more frequently than any other itemspurchased with the first item. As another example, if the currenttransaction cannot be associated with a buyer profile, then more generaltransaction information may be relied on, such as items that theparticular merchant has most frequently sold with the first item, oritems that other merchants, similar to the particular merchant, havesold most frequently with the first item. As still another example, ifthe current transaction can be associated with a buyer profile, but theparticular buyer has never bought the first item, or has never bought asecond item with the first item, then buyer profiles of buyers similarto the first buyer may be relied on for determining a second item thatis most frequently purchased with the first item by the buyers that aresimilar to the first buyer. For example, plurality of buyercharacteristics may be used for determining a subset of buyer profilesthat are similar to the buyer profile of the current buyer. Numerousother variations will be apparent to those of skill in the art havingthe benefit of the disclosure herein.

At 814, the computing device may send a communication to the merchantdevice of the particular merchant based at least in part on the currenttransaction information. For example, the communication may be sent tothe first merchant device, prior to completion of the current POStransaction, and the communication may identify the second item as acandidate item for inclusion in the current POS transaction. Themerchant device may present a recommendation included with thecommunication on a display of the merchant device, and the merchant maydetermine whether not to offer the second item to the current buyerbased on the recommendation. Furthermore, in some examples, therecommendation may include a bundle recommendation for bundling one ormore items with the first item at a reduced price.

FIG. 9 is a flow diagram illustrating an example process 900 forproviding recommendations to merchants according to someimplementations. In some examples, the process may be executed by themerchant device or by another suitable computing device.

At 902, the merchant device may send, from the merchant device, to aservice computing device, transaction information for a plurality ofpoint of sale (POS) transactions conducted using the merchant device.For example, for each individual transaction conducted on the merchantdevice, the merchant device may send the transaction information for thetransaction to the service computing device. As mentioned above, thetransaction information may include item information and, whenavailable, buyer information.

At 904, the merchant device may receive, from the service computingdevice, a plurality of predetermined recommendations for items to beoffered together. For example, for a list of items offered by themerchant, the service computing device may determine which items arefrequently sold together, e.g. with a frequency greater than a thresholdfrequency, and may provide a plurality of predetermined recommendationsfor these items to the merchant device. As mentioned above, the merchantdevice may store these predetermined recommendations in a local storageor the like.

At 906, the merchant device may determine, for a current POStransaction, a first item selected for the current POS transaction. Forexample, subsequent to receiving the predetermined recommendations, themerchant device may be used to conduct a current point of saletransactions with a current buyer.

At 908, the merchant device may determine whether the merchant device iscurrently operating in an online or offline mode. For instance, in somesituations, the merchant device may operate in an offline mode in whichthe merchant device is not connected to the Internet or other wide areanetwork of the one or more networks 106. Thus, the merchant device canbe used to complete transactions with buyers while in the offline modeand then may subsequently send the information about the completedtransactions to the service computing device when reconnected to asuitable network for communication with the service computing device.

At 910, when the merchant device is in the offline mode, the merchantdevice may determine, based on a first item selected by the currentbuyer, a second item from the predetermined recommendations to recommendas a candidate for inclusion in the current transaction. For instance,the merchant device may access the predetermined recommendations anddetermine which other items offered by the merchant are recommended tobe offered with the first item selected by the current buyer.

At 912, the merchant device may present on a display associated with themerchant device, a recommendation for a second item to be offered to thebuyer based on the buyer selection of the first item. Accordingly, themerchant device may recommend one or more items to be offered to thebuyer based on the predetermined recommendations previously receivedfrom the service computing device.

At 914, alternatively, when the merchant device is in the online mode,i.e., is able to communicate with the service computing device, themerchant device may send the current transaction information to theservice computing device.

At 916, the merchant device may receive an indication of a second itemfrom the service computing device based at least in part on the currenttransaction information sent to the service computing device. Forexample, the merchant device may receive one or more recommendationsfrom the service computing device of one or more items that may beoffered to the current buyer based on the current buyer selection of thefirst item.

At 918, the merchant device may present on a display associated with themerchant device, one or more recommendations based on the indicatedsecond item received from the service computing device. In someexamples, the recommendations may be text-based recommendationspresented on the display, such as overlaid on a payment processing userinterface, as discussed above, or using any other suitable techniquesfor conveying the recommendation information to the merchant.

The example processes described herein are only examples of processesprovided for discussion purposes. Numerous other variations will beapparent to those of skill in the art in light of the disclosure herein.Further, while the disclosure herein sets forth several examples ofsuitable frameworks, architectures and environments for executing theprocesses, implementations herein are not limited to the particularexamples shown and discussed. Furthermore, this disclosure providesvarious example implementations, as described and as illustrated in thedrawings. However, this disclosure is not limited to the implementationsdescribed and illustrated herein, but can extend to otherimplementations, as would be known or as would become known to thoseskilled in the art.

FIG. 10 illustrates an example architecture of a payment and itemrecommendation system 1000 able to provide a payment and itemrecommendation service according to some implementations. In the exampleof FIG. 10, the service computing device 102 of a service provider 1002includes the payment processing module 216, which may be executed toprovide the payment and transaction functionality, as described herein.The payment processing module 216 and corresponding paymentfunctionality may be implemented as one or more computer programs, orother executable instructions, on the service computing device 102 inone or more locations, such as for providing the payment systems,components, and techniques described herein.

The example of FIG. 10 illustrates at least one buyer device 116 and atleast one merchant device 104. For example, each buyer device 116 may beassociated with a participating buyer 114 p that participates in thepayment system of the service provider 1002. The buyer device 116 mayinclude the buyer application 118, as previously discussed herein, whichmay include an electronic payment module 208 that provides functionalityfor enabling the buyer 114 p to make electronic payments using the buyerdevice 116. In some examples, the buyer application 118 may includevarious other applications or modules, such as for a buyer dashboard toenable the buyer to control information in the buyer's profile, setbuyer preferences, and so forth. Further, the merchant device 104 may beassociated with a merchant 108 that participates in the payment serviceprovided by the service provider 1002, and the merchant device 104 mayinclude the merchant application 110. As discussed elsewhere herein, thebuyer device 116 and the merchant device 104 can each be a computingdevice able to communicate with each other, with the service computingdevice 102, and with various other computing devices, through anysuitable communication protocols, interfaces, and networks, includingthe one or more communication networks 106.

The buyer device 116 and the merchant device 104 can each include one ormore components, e.g., software or hardware, that are configured torespectively determine a geographic location of the buyer device 116and/or the merchant device 104, using, for example, various geolocationtechniques, e.g., a global positioning system (GPS), cell towerlocation, wireless access point location, wireless beacon location, andso forth. Further, the buyer device 116 and the merchant device 104 caneach be any appropriate device operable to send and receive requests,messages, or other types of information over the one or more networks106 or directly to each other. Some examples of buyer devices 116 andmerchant devices 104 are enumerated below. Additionally, while only asingle buyer device 116 and a single merchant device 104 are illustratedin the example of FIG. 10, in some implementation, there may bethousands, hundreds of thousands, or more, of the buyer devices 116 andthe merchant devices 104, depending on the number of the participatingbuyers 114 p and the number of merchants 108.

The one or more networks 106 can include any appropriate network,including a wide area network, such as the Internet; a local areanetwork, such an intranet; a wireless network, such as a cellularnetwork, a local wireless network, such as Wi-Fi and/or close-rangewireless communications, such as Bluetooth® and Bluetooth® low energy; awired network; or any other such network, or any combination thereof.Accordingly, the one or more networks 106 may include both wired and/orwireless communication technologies, including Bluetooth®, Bluetooth®low energy, Wi-Fi and cellular communication technologies, as well aswired or fiber optic technologies. Components used for suchcommunications can depend at least in part upon the type of network, theenvironment selected, or both. Protocols for communicating over suchnetworks are well known and will not be discussed herein in detail.Accordingly, the service computing device 102, the merchant devices 104,the buyer devices 116, and the other computing devices discussed hereinare able to communicate over the one or more networks 106 using wired orwireless connections, and combinations thereof.

Additionally, in some examples, information may also be obtained withrespect to non-participating buyers 114 np that do not have an accountwith the payment service provided through the service computing device102. The transaction information collected with respect to these buyersmay be sent to the service computing device 102, and buyer profiles maybe created for the nonparticipating buyers 114 np, as discussed above.Should one or more of the non-participating buyers later become aparticipating buyer, such as by signing up for the electronic paymentservice, the transaction information of an existing buyer profile forthat buyer may be merged with the newly created profile using theinformation matching and probabilistic modeling techniques describedabove with respect to FIGS. 3-5. In addition, in some examples,transaction information may be obtained with respect tonon-participating merchants (not shown) that do not use a merchantdevice 104, and this transaction information may be employed whendetermining item recommendations for the merchants 108.

When paying for a transaction, the buyer 114 can provide the amount ofpayment that is due to the merchant 108 using cash, check, a paymentcard, or by electronic payment using the buyer application 118 on thebuyer device 116. The merchant 108 can interact with the merchant device104 to process the transaction. During POS transactions 204, themerchant device 104 can determine and send data describing thetransactions, including, for example, the item(s) being purchased, theamount of the item(s), buyer information, and so forth. In someimplementations, the payment and merchant recommendation service enablescard-less payments, i.e., electronic payments, for transactions betweenthe participating buyers 114 p and the merchants 108 based oninteraction of the buyer 114 p with the buyer application 118 andinteraction of the merchant 108 with the merchant application 110.Accordingly, in some examples, a card-less payment transaction mayinclude a transaction conducted between a participating buyer 114 p anda merchant 108 at a POS location during which an electronic paymentaccount of the buyer 114 p is charged without the buyer 114 p having tophysically present a payment card to the merchant 108 at the POSlocation. Consequently, the merchant 108 need not receive any detailsabout the financial account of the buyer 114 p for the transaction to beprocessed. As one example, the electronic payment may be charged to acredit card issuer or credit card number that the participating buyer114 p provided when signing up with the service provider for theelectronic payment account. As another example, the buyer 114 p may havea quantity of money pre-paid in an account maintained for use in makingthe electronic payments. Other variations will also be apparent to thoseof skill in the art having the benefit of the disclosure herein.

Before conducting an electronic payment transaction, the participatingbuyer 114 p typically creates a user account with service provider ofthe payment and item recommendation service. The participating buyer 114p can create the user account, for example, by interacting with thebuyer application 118 that is configured to perform electronic paymenttransactions and that may execute on the buyer device 116. When creatinga buyer electronic payment account with the payment service, theparticipating buyer 114 p may provide an image including the face of thebuyer, data describing a financial account of the buyer 114 p, e.g., acredit card number, expiration date, and a billing address. This userinformation can be securely stored by the payment service, for example,in the buyer information 126, such as in a secure database. Further, thebuyer profiles 128 may be created for each buyer 114, which may includeinformation about the buyer and transactions conducted by the buyer.

To accept electronic payments for POS transactions, the merchant 108typically creates a merchant account with the payment and itemrecommendation service by providing information describing the merchantincluding, for example, a merchant name, contact information, e.g.,telephone numbers, the merchant's geographic location address, and oneor more financial accounts to which funds collected from buyers will bedeposited. This merchant information can be securely stored by thepayment service, for example, in the merchant information 122, such asin a secure database. Further, a merchant profile 124 may be created foreach merchant, which may include information about the merchant andtransactions conducted by the merchant.

The payment service is configured to enable electronic payments fortransactions. The payment service can include one or more servers thatare configured to perform securely electronic financial transactions,e.g., electronic payments for transactions between a buyer and amerchant, for example, through data communicated between the buyerdevice 116 and the merchant device 104. Generally, when a buyer and amerchant enter into an electronic payment transaction, the transactionis processed by electronically transferring funds from a financialaccount associated with the user account to a financial accountassociated with the merchant account.

The payment and item recommendation service is configured to send andreceive data to and from the buyer device 116 and the merchant device104. For example, the payment and item recommendation service can beconfigured to send information describing merchants to the buyerapplication 118 on the buyer device 116 using, for example, theinformation stored in the merchant information 122. For example, thepayment and item recommendation service can communicate data describingmerchants 108 that are within a threshold geographic distance from ageographic location of the buyer device 116. The data describing themerchants 108 can include, for example, a merchant name, geographiclocation, contact information, and an electronic catalogue, e.g., a menuthat describes items that are available for purchase from the merchant.

In some embodiments, the payment and item recommendation system 1000 isconfigured to determine whether a geographic location of the buyerdevice 116 is within a threshold geographic distance from a geographiclocation of the merchant device 104. The payment and item recommendationsystem 1000 can determine a geographic location of the buyer device 116using, for example, geolocation data provided by the buyer device 116.Similarly, the payment and item recommendation system 1000 can determinea geographic location of the merchant device 104 using, for example,geolocation data provided by the merchant device 104 or using ageographic address, e.g., street address, provided by the merchant.Depending on the implementation, the threshold geographic distance canbe specified by the payment and item recommendation system 1000, by thebuyer, or by the merchant.

Determining whether the buyer device 116 is within a thresholdgeographic distance of the merchant device 104 can be accomplished indifferent ways including, for example, determining whether the buyerdevice 116 is within a threshold geographic radius of the merchantdevice 104, determining whether the buyer device 116 is within aparticular geofence, or determining whether the buyer device 116 cancommunicate with the merchant device 104 using a specified wirelesstechnology, e.g., Bluetooth® or Bluetooth® low energy (BLE). In someembodiments, the payment and item recommendation system 1000 restrictselectronic payment transactions between the participating buyer 114 pand the merchant 108 to situations where the geographic location of thebuyer device 116 is within a threshold geographic distance from ageographic location of the merchant device 104.

The payment and item recommendation system 1000 can also be configuredto communicate with one or more computing devices 1004 of a card paymentnetwork (e.g., MasterCard®, VISA®) over the one or more networks 106 toconduct financial transactions electronically. The payment and itemrecommendation system 1000 can also communicate with one or more bankcomputing devices 1006 of one or more banks over the one or morenetworks 106. For example, the payment and item recommendation system1000 may communicate with an acquiring bank, and/or an issuing bank,and/or a bank maintaining buyer accounts for electronic payments.

An acquiring bank may be a registered member of a card association(e.g., Visa®, MasterCard®), and may be part of a card payment network.An issuing bank may issue payment cards to buyers, and may pay acquiringbanks for purchases made by cardholders to which the issuing bank hasissued a payment card. Accordingly, in some examples, the computingdevice(s) of an acquiring bank may be included in the card paymentnetwork and may communicate with the computing devices of a card-issuingbank to obtain payment. Further, in some examples, the buyer may use adebit card instead of a credit card, in which case, the bank computingdevice(s) of a bank corresponding to the debit card may receivecommunications regarding a transaction in which the buyer isparticipating. Additionally, there may be computing devices of otherfinancial institutions involved in some types of transactions or inalternative system architectures, and thus, the foregoing are merelyseveral examples for discussion purposes.

The participating buyer 114 p operating the buyer device 116 that iswithin a threshold geographic distance of the merchant device 104 caninteract with the buyer application 118 executed on the buyer device 116to conduct an electronic payment transaction with the merchant 108.While interacting with the buyer application 118, the buyer 114 p canselect the merchant 108, from a listing of merchants 108, with whom thebuyer 114 p wants to enter into an electronic payment transaction. Thebuyer 114 p can select the merchant 108, for example, by selecting a“check in” option associated with the merchant 108. The buyer device 116can communicate data to the payment and item recommendation system 1000indicating that the buyer 114 p has checked in with the merchant 108. Inresponse, the payment and item recommendation system 1000 cancommunicate data to notify the merchant device 104 that the buyer haschecked in. The merchant application 110 executing on the merchantdevice 104 can notify the merchant 108 that the buyer has electronicallychecked in with the merchant 108 through a display screen of themerchant device 104.

Once checked in, the buyer 114 p can obtain, or request, items that areavailable to be acquired from the merchant 108. When the buyer 114 p isready to enter into the card-less payment transaction, the buyer 114 pcan, for example, approach a point of sale for the merchant 108 andidentify him or herself. For example, the buyer 114 p can verballynotify the merchant 108 that the buyer 114 p wants to enter into acard-less payment transaction and can provide the merchant 108 with thebuyer's name. The merchant 108 can then interact with the merchantapplication 110 to select the buyer 114 p, from a listing of buyers thathave checked in with the merchant 108, to initiate an electronic paymenttransaction for the item(s) being acquired by the buyer 114 p. Forexample, the merchant 108 can determine a total amount to charge thebuyer 114 p for the item(s) being acquired. The buyer 114 p can verballyapprove the total amount to be paid and, in response, the merchant 108can submit a request for an electronic payment transaction for the totalamount of the transaction to the payment and item recommendation system1000. In response, the payment and item recommendation system 1000 canobtain, for example, from the buyer information 126, data describing afinancial account associated with the electronic purchase account of thebuyer 114 p to which the total amount will be charged.

The payment and item recommendation system 1000 can then communicatewith the computing device 1004 of a card payment network to complete anelectronic payment transaction for the total amount to be charged to thebuyer's electronic payment account. Once the electronic paymenttransaction is complete, the payment and item recommendation system 1000can communicate data describing the electronic payment for thetransaction to the buyer device 116, e.g., as an electronic receipt,which can, for example, notify the buyer 114 p of the total amountcharged to the buyer for the electronic payment for the transaction withthe particular merchant. Further, while a mobile buyer device 116 isdescribed in this example for purposes of explanation, additional oralternative types of devices may be used in other examples.

In addition, in some examples, the service provider 1002 may makeavailable one or more service provider websites 1018 that enablemerchants 108 to advertise items on the service provider website(s). Forexample, merchants 108 may offer items for purchase to buyers on thewebsite. The buyers may purchase the items using a web browser, or otherapplication on a computing device, such as the buyer device 116 or othercomputing device. The transaction information from these transactionsmay be provided to the service computing device 102 to add further tothe transaction information in the buyer profiles 128 and the merchantprofiles 124.

In addition, the analysis module 218 and/or the item recommendationmodule 130 may access other websites 1010 when determining informationabout buyers and/or when determining item recommendations for merchants,respectively. For example, demographic information and other buyerinformation may be obtained from the US Census Bureau website, socialnetwork sites, a microblog site, or other online presences of thevarious different buyers. Similarly, geographic information may beobtained from websites that provide maps and other geographic ordemographic information, or the like.

FIG. 11 illustrates select components of the service computing device102 that may be used to implement some functionality of the payment anditem recommendation service described herein. The service computingdevice 102 may be operated by a service provider that provides thepayment service and the item recommendation service, and may include oneor more servers or other types of computing devices that may be embodiedin any number of ways. For instance, in the case of a server, themodules, other functional components, and data may be implemented on asingle server, a cluster of servers, a server farm or data center, acloud-hosted computing service, a cloud-hosted storage service, and soforth, although other computer architectures may additionally oralternatively be used.

Further, while the figures illustrate the components and data of theservice computing device 102 as being present in a single location,these components and data may alternatively be distributed acrossdifferent computing devices and different locations in any manner.Consequently, the functions may be implemented by one or more servicecomputing devices, with the various functionality described abovedistributed in various ways across the different computing devices.Multiple service computing devices 102 may be located together orseparately, and organized, for example, as virtual servers, server banksand/or server farms. The described functionality may be provided by theservers of a single entity or enterprise, or may be provided by theservers and/or services of multiple different buyers or enterprises.

In the illustrated example, each service computing device 102 mayinclude one or more processors 1102, one or more computer-readable media1104, and one or more communication interfaces 1106. Each processor 1102may be a single processing unit or a number of processing units, and mayinclude single or multiple computing units or multiple processing cores.The processor(s) 1102 can be implemented as one or more microprocessors,microcomputers, microcontrollers, digital signal processors, centralprocessing units, state machines, logic circuitries, and/or any devicesthat manipulate signals based on operational instructions. For instance,the processor(s) 1102 may be one or more hardware processors and/orlogic circuits of any suitable type specifically programmed orconfigured to execute the algorithms and processes described herein. Theprocessor(s) 1102 can be configured to fetch and executecomputer-readable instructions stored in the computer-readable media1104, which can program the processor(s) 1102 to perform the functionsdescribed herein.

The computer-readable media 1104 may include volatile and nonvolatilememory and/or removable and non-removable media implemented in any typeof technology for storage of information, such as computer-readableinstructions, data structures, program modules, or other data. Suchcomputer-readable media 1104 may include, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, optical storage,solid state storage, magnetic tape, magnetic disk storage, RAID storagesystems, storage arrays, network attached storage, storage areanetworks, cloud storage, or any other medium that can be used to storethe desired information and that can be accessed by a computing device.Depending on the configuration of the service computing device 102, thecomputer-readable media 1104 may be a type of computer-readable storagemedia and/or may be a tangible non-transitory media to the extent thatwhen mentioned, non-transitory computer-readable media exclude mediasuch as energy, carrier signals, electromagnetic waves, and signals perse.

The computer-readable media 1104 may be used to store any number offunctional components that are executable by the processors 1102. Inmany implementations, these functional components comprise instructionsor programs that are executable by the processors 1102 and that, whenexecuted, specifically configure the one or more processors 1102 toperform the actions attributed above to the service computing device102. Functional components stored in the computer-readable media 1104may include the item recommendation module 130, the analysis module 218,and the payment processing module 216. Additional functional componentsstored in the computer-readable media 1104 may include an operatingsystem 1108 for controlling and managing various functions of theservice computing device 102.

In addition, the computer-readable media 1104 may store data used forperforming the operations described herein. Thus, the computer-readablemedia may store the merchant information 122, including the merchantprofiles 124, and the buyer information 126, including the buyerprofiles 128. In addition, at least a portion of the probabilistic model300 may be stored on the computer-readable media and/or the servicecomputing device 102 may access or generate the probabilistic model 300.The service computing device 102 may also include or maintain otherfunctional components and data, such as other modules and data 1110,which may include programs, drivers, etc., and the data used orgenerated by the functional components. Further, the service computingdevice 102 may include many other logical, programmatic and physicalcomponents, of which those described above are merely examples that arerelated to the discussion herein.

The communication interface(s) 1106 may include one or more interfacesand hardware components for enabling communication with various otherdevices, such as over the network(s) 106. For example, communicationinterface(s) 1106 may enable communication through one or more of theInternet, cable networks, cellular networks, wireless networks (e.g.,Wi-Fi) and wired networks, as well as close-range communications such asBluetooth®, Bluetooth® low energy, and the like, as additionallyenumerated elsewhere herein.

The service computing device 102 may further be equipped with variousinput/output (I/O) devices 1112. Such I/O devices 1112 may include adisplay, various user interface controls (e.g., buttons, joystick,keyboard, mouse, touch screen, etc.), audio speakers, connection portsand so forth.

FIG. 12 illustrates select example components of an example merchantdevice 104 according to some implementations. The merchant device 104may be any suitable type of computing device, e.g., portable,semi-portable, semi-stationary, or stationary. Some examples of themerchant device 104 may include tablet computing devices; smart phonesand mobile communication devices; laptops, netbooks and other portablecomputers or semi-portable computers; desktop computing devices,terminal computing devices and other semi-stationary or stationarycomputing devices; dedicated register devices; wearable computingdevices, or other body-mounted computing devices; augmented realitydevices; or other computing devices capable of sending communicationsand performing the functions according to the techniques describedherein.

In the illustrated example, the merchant device 104 includes at leastone processor 1202, one or more computer-readable media 1204, one ormore communication interfaces 1206, and one or more input/output (I/O)devices 1208. Each processor 1202 may itself comprise one or moreprocessors or processing cores. For example, the processor 1202 can beimplemented as one or more microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units,state machines, logic circuitries, and/or any devices that manipulatesignals based on operational instructions. In some cases, the processor1202 may be one or more hardware processors and/or logic circuits of anysuitable type specifically programmed or configured to execute thealgorithms and processes described herein. The processor 1202 can beconfigured to fetch and execute computer-readable processor-executableinstructions stored in the computer-readable media 1204.

Depending on the configuration of the merchant device 104, thecomputer-readable media 1204 may be an example of tangiblenon-transitory computer storage media and may include volatile andnonvolatile memory and/or removable and non-removable media implementedin any type of technology for storage of information such ascomputer-readable processor-executable instructions, data structures,program modules or other data. The computer-readable media 1204 mayinclude, but is not limited to, RAM, ROM, EEPROM, flash memory,solid-state storage, magnetic disk storage, optical storage, and/orother computer-readable media technology. Further, in some cases, themerchant device 104 may access external storage, such as RAID storagesystems, storage arrays, network attached storage, storage areanetworks, cloud storage, or any other medium that can be used to storeinformation and that can be accessed by the processor 1202 directly orthrough another computing device or network. Accordingly, thecomputer-readable media 1204 may be computer storage media able to storeinstructions, modules or components that may be executed by theprocessor 1202. Further, when mentioned, non-transitorycomputer-readable media exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

The computer-readable media 1204 may be used to store and maintain anynumber of functional components that are executable by the processor1202. In some implementations, these functional components compriseinstructions or programs that are executable by the processor 1202 andthat, when executed, implement operational logic for performing theactions and services attributed above to the merchant device 104.Functional components of the merchant device 104 stored in thecomputer-readable media 1204 may include the merchant application 110.In this example, the merchant application 110 includes a transactionmodule 1210 and a merchant dashboard module 1212. For example, thetransaction module 1210 may present an interface, such as the paymentinterface 706 discussed above, to enable the merchant to conducttransactions, receive payments, and so forth, as well as communicatingwith the service computing device 102 for processing payments andsending transaction information. Further, the merchant dashboard module1212 may present an interface to enable the merchant to manage themerchant's account, the merchant profile, merchant preferences, viewsaved or new item recommendations, and the like. Additional functionalcomponents may include an operating system 1214 for controlling andmanaging various functions of the merchant device 104 and for enablingbasic user interactions with the merchant device 104.

In addition, the computer-readable media 1204 may also store data, datastructures and the like, that are used by the functional components. Forexample, data stored by the computer-readable media 1204 may includeitem information 1216 that includes information about the items offeredby the merchant, which may include a list of items currently availablefrom the merchant, images of the items, descriptions of the items,prices of the items, and so forth. Furthermore, the computer readablemedia may have stored thereon predetermined recommendations 1218 thatmay be received from the service provider in advance to enable themerchant device 104 to provide recommendations in an offline mode, orthe like. Depending on the type of the merchant device 104, thecomputer-readable media 1204 may also optionally include otherfunctional components and data, such as other modules and data 1220,which may include programs, drivers, etc., and the data used orgenerated by the functional components. Further, the merchant device 104may include many other logical, programmatic and physical components, ofwhich those described are merely examples that are related to thediscussion herein.

The communication interface(s) 1206 may include one or more interfacesand hardware components for enabling communication with various otherdevices, such as over the network(s) 106 or directly. For example,communication interface(s) 1206 may enable communication through one ormore of the Internet, cable networks, cellular networks, wirelessnetworks (e.g., Wi-Fi) and wired networks, as well as close-rangecommunications such as Bluetooth®, Bluetooth® low energy, and the like,as additionally enumerated elsewhere herein.

FIG. 12 further illustrates that the merchant device 104 may include thedisplay 138 mentioned above. Depending on the type of computing deviceused as the merchant device 104, the display 138 may employ any suitabledisplay technology. For example, the display 138 may be a liquid crystaldisplay, a plasma display, a light emitting diode display, an OLED(organic light-emitting diode) display, an electronic paper display, orany other suitable type of display able to present digital contentthereon. In some examples, the display 138 may have a touch sensorassociated with the display 138 to provide a touchscreen displayconfigured to receive touch inputs for enabling interaction with agraphic interface presented on the display 138. Accordingly,implementations herein are not limited to any particular displaytechnology. Alternatively, in some examples, the merchant device 104 maynot include the display 138, and information may be present by othermeans, such as aurally.

The merchant device 104 may further include the one or more I/O devices1208. The I/O devices 1208 may include speakers, a microphone, a camera,and various user controls (e.g., buttons, a joystick, a keyboard, akeypad, etc.), a haptic output device, and so forth.

In addition, the merchant device 104 may include or may be connectableto a card reader 1222. In some examples, the card reader may plug in toa port in the merchant device, such as a microphone/headphone port, adata port, or other suitable port. The card reader may include a readhead for reading a magnetic strip of a payment card, and further mayinclude encryption technology for encrypting the information read fromthe magnetic strip. Alternatively, numerous other types of card readersmay be employed with the merchant devices 104 herein, depending on thetype and configuration of the merchant device 104.

Other components included in the merchant device 104 may include varioustypes of sensors, which may include a GPS device 1224 able to indicatelocation information, as well as other sensors (not shown) such as anaccelerometer, gyroscope, compass, proximity sensor, and the like.Additionally, the merchant device 104 may include various othercomponents that are not shown, examples of which include removablestorage, a power source, such as a battery and power control unit, andso forth.

FIG. 13 illustrates select example components of the buyer device 116that may implement the functionality described above according to someexamples. The buyer device 116 may be any of a number of different typesof portable computing devices. Some examples of the buyer device 116 mayinclude smart phones and mobile communication devices; tablet computingdevices; laptops, netbooks and other portable computers; wearablecomputing devices and/or body-mounted computing devices, which mayinclude watches and augmented reality devices, such as helmets, gogglesor glasses; and any other portable device capable of sendingcommunications and performing the functions according to the techniquesdescribed herein.

In the example of FIG. 13, the buyer device 116 includes components suchas at least one processor 1302, one or more computer-readable media1304, the one or more communication interfaces 1306, and one or moreinput/output (I/O) devices 1314. Each processor 1302 may itself compriseone or more processors or processing cores. For example, the processor1302 can be implemented as one or more microprocessors, microcomputers,microcontrollers, digital signal processors, central processing units,state machines, logic circuitries, and/or any devices that manipulatesignals based on operational instructions. In some cases, the processor1302 may be one or more hardware processors and/or logic circuits of anysuitable type specifically programmed or configured to execute thealgorithms and processes described herein. The processor 1302 can beconfigured to fetch and execute computer-readable processor-executableinstructions stored in the computer-readable media 1304.

Depending on the configuration of the buyer device 116, thecomputer-readable media 1304 may be an example of tangiblenon-transitory computer storage media and may include volatile andnonvolatile memory and/or removable and non-removable media implementedin any type of technology for storage of information such ascomputer-readable processor-executable instructions, data structures,program modules or other data. The computer-readable media 1304 mayinclude, but is not limited to, RAM, ROM, EEPROM, flash memory,solid-state storage, magnetic disk storage, optical storage, and/orother computer-readable media technology. Further, in some cases, thebuyer device 116 may access external storage, such as RAID storagesystems, storage arrays, network attached storage, storage areanetworks, cloud storage, or any other medium that can be used to storeinformation and that can be accessed by the processor 1302 directly orthrough another computing device or network. Accordingly, thecomputer-readable media 1304 may be computer storage media able to storeinstructions, modules or components that may be executed by theprocessor 1302. Further, when mentioned, non-transitorycomputer-readable media exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

The computer-readable media 1304 may be used to store and maintain anynumber of functional components that are executable by the processor1302. In some implementations, these functional components compriseinstructions or programs that are executable by the processor 1302 andthat, when executed, implement operational logic for performing theactions and services attributed above to the buyer device 116.Functional components of the buyer device 116 stored in thecomputer-readable media 1304 may include the buyer application 118, asdiscussed above. In this example, the buyer application 118 includes theelectronic payment module 208, as discussed above, and a buyer dashboardmodule 1310. For example, the buyer dashboard module 1310 may presentthe buyer with an interface for managing the buyer's account, changinginformation, changing preferences, and so forth. Additional functionalcomponents may include an operating system 1312 for controlling andmanaging various functions of the buyer device 116 and for enablingbasic user interactions with the buyer device 116.

In addition, the computer-readable media 1304 may also store data, datastructures and the like, that are used by the functional components.Depending on the type of the buyer device 116, the computer-readablemedia 1304 may also optionally include other functional components anddata, such as other modules and data 1306, which may includeapplications, programs, drivers, etc., and the data used or generated bythe functional components. Further, the buyer device 116 may includemany other logical, programmatic and physical components, of which thosedescribed are merely examples that are related to the discussion herein.

The communication interface(s) 1306 may include one or more interfacesand hardware components for enabling communication with various otherdevices, such as over the network(s) 106 or directly. For example,communication interface(s) 1306 may enable communication through one ormore of the Internet, cable networks, cellular networks, wirelessnetworks (e.g., Wi-Fi) and wired networks, as well as close-rangecommunications such as Bluetooth®, Bluetooth® low energy, and the like,as additionally enumerated elsewhere herein.

FIG. 13 further illustrates that the buyer device 116 may include adisplay 1316. Depending on the type of computing device used as thebuyer device 116, the display may employ any suitable displaytechnology. For example, the display 1316 may be a liquid crystaldisplay, a plasma display, a light emitting diode display, an OLED(organic light-emitting diode) display, an electronic paper display, orany other suitable type of display able to present digital contentthereon. In some examples, the display 1316 may have a touch sensorassociated with the display 1316 to provide a touchscreen displayconfigured to receive touch inputs for enabling interaction with agraphic interface presented on the display 1316. Accordingly,implementations herein are not limited to any particular displaytechnology. Alternatively, in some examples, the buyer device 116 maynot include a display.

The buyer device 116 may further include the one or more I/O devices1308. The I/O devices 1308 may include speakers, a microphone, a camera,and various user controls (e.g., buttons, a joystick, a keyboard, akeypad, etc.), a haptic output device, and so forth.

Other components included in the buyer device 116 may include varioustypes of sensors, which may include a GPS device 1318 able to indicatelocation information, as well as other sensors (not shown) such as anaccelerometer, gyroscope, compass, proximity sensor, and the like.Additionally, the buyer device 116 may include various other componentsthat are not shown, examples of which include removable storage, a powersource, such as a battery and power control unit, and so forth.

Various instructions, methods and techniques described herein may beconsidered in the general context of computer-executable instructions,such as program modules stored on computer-readable media, and executedby the processor(s) herein. Generally, program modules include routines,programs, objects, components, data structures, etc., for performingparticular tasks or implementing particular abstract data types. Theseprogram modules, and the like, may be executed as native code or may bedownloaded and executed, such as in a virtual machine or otherjust-in-time compilation execution environment. Typically, thefunctionality of the program modules may be combined or distributed asdesired in various implementations. An implementation of these modulesand techniques may be stored on computer storage media or transmittedacross some form of communication media.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as example forms ofimplementing the claims.

What is claimed is:
 1. A method, implemented in part by a servercomputing device of a payment processing service, the method comprising:storing, in a database associated with the server computing device,buyer profiles associated with buyers involved in transactions withmerchants of the payment processing service; receiving, by the servercomputing device, transaction data associated with a new transactionbetween a buyer and a merchant; determining, by the server computingdevice, a correspondence between the transaction data and at least twobuyer profiles of the buyer profiles associated with differentidentifiers; based at least in part on determining that thecorrespondence between the transaction data and the at least two buyerprofiles of the buyer profiles satisfies a confidence threshold,determining, by the server computing device, that a first buyer profileof the at least two buyer profiles corresponds to a second buyer profileof the at least two buyer profiles; performing, by the server computingdevice, a conflict analysis to determine whether mapping the first buyerprofile to the second buyer profile causes a conflict; mapping, by theserver computing device and based at least in part on the conflictanalysis, information associated with the first buyer profile to thesecond buyer profile, wherein a resulting buyer profile includes thesecond buyer profile and at least a portion of data included in thefirst buyer profile; determining, by the server computing device, arecommendation for the new transaction based at least in part on theresulting buyer profile; and sending, from the server computing device,the recommendation to a computing device associated with at least one ofthe merchant or the buyer prior to completion of the new transaction. 2.The method as claim 1 recites, wherein the transaction data comprises atleast one of an email address, a payment card identifier, a telephonenumber, a payment account identifier, or a merchant incentive programidentifier.
 3. The method as claim 1 recites, wherein determining thecorrespondence between the transaction data and the at least two buyerprofiles comprises: determining that a first data item of thetransaction data corresponds to the first buyer profile; and determiningthat a second data item of the transaction data corresponds to thesecond buyer profile, wherein the first data item and the second dataitem are associated with the different identifiers.
 4. The method asclaim 3 recites, wherein the first data item and the second data itemare high-confidence identifiers.
 5. The method as claim 1 recites,wherein the recommendation comprises an item recommendation.
 6. Themethod as claim 1 recites, wherein the transaction data is received froma merchant device at a store of the merchant.
 7. The method as claim 1recites, further comprising, based at least in part on mapping theinformation associated with the first buyer profile to the second buyerprofile, deleting the first buyer profile.
 8. A system associated with apayment processing service, the system comprising: one or moreprocessors; and computer-readable media storing instructions, that whenexecuted by the one or more processors, cause the one or more processorsto perform operations comprising: storing, in a database associated withthe system, buyer profiles associated with buyers involved intransactions with merchants of the payment processing service; receivingtransaction data associated with a new transaction between a buyer and amerchant; determining a correspondence between the transaction data andat least two buyer profiles of the buyer profiles associated withdifferent identifiers; based at least in part on determining that thecorrespondence between the transaction data and the at least two buyerprofiles of the buyer profiles satisfies a confidence threshold,determining that a first buyer profile of the at least two buyerprofiles corresponds to a second buyer profile of the at least two buyerprofiles; performing a conflict analysis to determine whether mappingthe first buyer profile to the second buyer profile causes a conflict;mapping, based at least in part on the conflict analysis, informationassociated with the first buyer profile to the second buyer profile,wherein a resulting buyer profile includes the second buyer profile andat least a portion of data included in the first buyer profile;determining a recommendation for the new transaction based at least inpart on the resulting buyer profile; and sending the recommendation to acomputing device associated with at least one of the merchant or thebuyer prior to completion of the new transaction.
 9. The system as claim8 recites, wherein the transaction data comprises at least one of anemail address, a payment card identifier, a telephone number, a paymentaccount identifier, or a merchant incentive program identifier.
 10. Thesystem as claim 8 recites, wherein determining the correspondencebetween the transaction data and the at least two buyer profilescomprises: determining that a first data item of the transaction datacorresponds to the first buyer profile; and determining that a seconddata item of the transaction data corresponds to the second buyerprofile, wherein the first data item and the second data item areassociated with the different identifiers.
 11. The system as claim 10recites, wherein the first data item and the second data item arehigh-confidence identifiers.
 12. The system as claim 8 recites, whereinthe recommendation comprises an item recommendation.
 13. The system asclaim 8 recites, wherein the transaction data is received from amerchant device at a store of the merchant.
 14. The system as claim 8recites, the operations further comprising, based at least in part onmapping the information associated with the first buyer profile to thesecond buyer profile, deleting the first buyer profile.
 15. One or morenon-transitory computer-readable media storing instructions, that whenexecuted by one or more processors, cause the one or more processors toperform operations comprising: storing, in a database associated withone or more server computing devices of a payment processing service,buyer profiles associated with buyers involved in transactions withmerchants of the payment processing service; receiving, by the one ormore server computing devices, transaction data associated with a newtransaction between a buyer and a merchant; determining, by the one ormore server computing devices, a correspondence between the transactiondata and at least two buyer profiles of the buyer profiles associatedwith different identifiers; based at least in part on determining thatthe correspondence between the transaction data and the at least twobuyer profiles of the buyer profiles satisfies a confidence threshold,determining, by the one or more server computing devices, that a firstbuyer profile of the at least two buyer profiles corresponds to a secondbuyer profile of the at least two buyer profiles; performing, by the oneor more server computing devices, a conflict analysis to determinewhether mapping the first buyer profile to the second buyer profilecauses a conflict; mapping, by the one or more server computing devicesand based at least in part on the conflict analysis, informationassociated with the first buyer profile to the second buyer profile,wherein a resulting buyer profile includes the second buyer profile andat least a portion of data included in the first buyer profile;determining, by the one or more server computing devices, arecommendation for the new transaction based at least in part on theresulting buyer profile; and sending, from the one or more servercomputing devices, the recommendation to a computing device associatedwith at least one of the merchant or the buyer prior to completion ofthe new transaction.
 16. The one or more non-transitorycomputer-readable media as claim 15 recites, wherein the transactiondata comprises at least one of an email address, a payment cardidentifier, a telephone number, a payment account identifier, or amerchant incentive program identifier.
 17. The one or morenon-transitory computer-readable media as claim 15 recites, whereindetermining the correspondence between the transaction data and the atleast two buyer profiles comprises: determining that a first data itemof the transaction data corresponds to the first buyer profile; anddetermining that a second data item of the transaction data correspondsto the second buyer profile, wherein the first data item and the seconddata item are associated with the different identifiers.
 18. The one ormore non-transitory computer-readable media as claim 17 recites, whereinthe first data item and the second data item are high-confidenceidentifiers.
 19. The one or more non-transitory computer-readable mediaas claim 15 recites, wherein the recommendation comprises an itemrecommendation.
 20. The one or more non-transitory computer-readablemedia as claim 15 recites, wherein the transaction data is received froma merchant device at a store of the merchant.